{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T06:52:18Z","timestamp":1785912738838,"version":"3.56.0"},"update-to":[{"DOI":"10.12688\/wellcomeopenres.24103.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000}},{"DOI":"10.12688\/wellcomeopenres.24103.2","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000}}],"reference-count":28,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["227486"],"award-info":[{"award-number":["227486"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001407","name":"Department of Biotechnology, Ministry of Science and Technology, India","doi-asserted-by":"publisher","award":["BT\/PR40144\/BTIS\/137\/46\/2022"],"award-info":[{"award-number":["BT\/PR40144\/BTIS\/137\/46\/2022"]}],"id":[{"id":"10.13039\/501100001407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001407","name":"Department of Biotechnology, Ministry of Science and Technology, India","doi-asserted-by":"publisher","award":["BT\/PR40150\/BTIS\/137\/81\/2023"],"award-info":[{"award-number":["BT\/PR40150\/BTIS\/137\/81\/2023"]}],"id":[{"id":"10.13039\/501100001407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["wellcomeopenresearch.org"],"crossmark-restriction":false},"short-container-title":["Wellcome Open Res"],"abstract":"<ns5:p>\n                    Background Shared Multidrug resistance (sMDR) is a major challenge in antimicrobial therapy, particularly among ESKAPEE pathogens, as these bacteria develop resistance to multiple antimicrobial agents via mutations, recombination, or gene transfer. These antimicrobial resistance (AMR) mechanisms evolve and require constant surveillance. Understanding sMDR patterns in large antimicrobial surveillance datasets is complex because of the multifaceted nature of the data. Methods We explored Pfizer-Atlas (2004 \u2013 2022) and Venatorx-GEARS (2018-2022) datasets from the Vivli platform, focusing on ESKAPEE pathogens. Descriptive data analysis, time-trend analysis, and association rule generation through the Apriori algorithm were performed using Python 3.10 libraries pandas, matplotlib, seaborn, scipy.stats, and mlxtend libraries. The best rule set that explored sMDR patterns was visualized in a network format using NetworkX: 3.2 package. Results \u201cMERIT- Multidrug ESKAPEE Resistance Insights and Tracker\u201d dashboard was created for user-friendly visualization of antimicrobial surveillance datasets, trends in antimicrobial susceptibility profile (ASP) over years, interactive widgets to see the ASP by country and by pathogens, and Network to see significant rules as a result of association rule mining in categories by age, year and country. Conclusion Time trend analysis revealed a decline in meropenem and piperacillin-tazobactam resistance to\n                    <ns5:italic>Enterococcus faecium<\/ns5:italic>\n                    and doripenem for\n                    <ns5:italic>Pseudomonas aeruginosa<\/ns5:italic>\n                    , while resistance to imipenem (\n                    <ns5:italic>Klebsiella pneumoniae<\/ns5:italic>\n                    ,\n                    <ns5:italic>Acinetobacter baumannii<\/ns5:italic>\n                    , and\n                    <ns5:italic>Enterobacter<\/ns5:italic>\n                    species) and meropenem (\n                    <ns5:italic>Staphylococcus aureus<\/ns5:italic>\n                    ,\n                    <ns5:italic>A. baumannii<\/ns5:italic>\n                    , and\n                    <ns5:italic>Enterobacter<\/ns5:italic>\n                    species) increased. Association rule mining identified sMDR patterns, such as meropenem and levofloxacin resistance, in\n                    <ns5:italic>S. aureus<\/ns5:italic>\n                    ,\n                    <ns5:italic>K. pneumoniae<\/ns5:italic>\n                    ,\n                    <ns5:italic>P. aeruginosa<\/ns5:italic>\n                    and\n                    <ns5:italic>A. baumannii.<\/ns5:italic>\n                    Thus, our findings from the data challenge represents hypothesis driven patterns intended to promote further investigation and clinical validation which potentially supports antibiotic stewardship efforts.\n                  <\/ns5:p>","DOI":"10.12688\/wellcomeopenres.24103.3","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T09:59:08Z","timestamp":1784282348000},"page":"461","update-policy":"https:\/\/doi.org\/10.12688\/wellcomeopenres.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the evolution of multidrug resistance patterns in ESKAPEE pathogens using association mining: Key to antibiotic stewardship?"],"prefix":"10.12688","volume":"10","author":[{"given":"Sasikala","family":"Rajendran","sequence":"first","affiliation":[{"name":"DBT-Bioinformatics Center, School of Chemical & Biotechnology, SASTRA Deemed to be University Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"},{"name":"Computational Biology lab, School of Chemical and Biotechnology, Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srimathy","family":"Ramachandran","sequence":"additional","affiliation":[{"name":"DBT-Bioinformatics Center, School of Chemical & Biotechnology, SASTRA Deemed to be University Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"},{"name":"Computational Biology lab, School of Chemical and Biotechnology, Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akilandeswari","family":"Ramu","sequence":"additional","affiliation":[{"name":"DBT-Bioinformatics Center, School of Chemical & Biotechnology, SASTRA Deemed to be University Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"},{"name":"Computational Molecular Biophysics Laboratory, School of Chemical & Biotechnology Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9033-5391","authenticated-orcid":false,"given":"Yashwenth","family":"S","sequence":"additional","affiliation":[{"name":"DBT-Bioinformatics Center, School of Chemical & Biotechnology, SASTRA Deemed to be University Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"},{"name":"School of Computing Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surya Murugan","family":"MS","sequence":"additional","affiliation":[{"name":"DBT-Bioinformatics Center, School of Chemical & Biotechnology, SASTRA Deemed to be University Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"},{"name":"School of Electrical & Electronics Engineering, Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vigneshwar","family":"Ramakrishnan","sequence":"additional","affiliation":[{"name":"DBT-Bioinformatics Center, School of Chemical & Biotechnology, SASTRA Deemed to be University Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"},{"name":"Computational Molecular Biophysics Laboratory, School of Chemical & Biotechnology Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7692-163X","authenticated-orcid":false,"given":"Suma","family":"Mohan S","sequence":"additional","affiliation":[{"name":"DBT-Bioinformatics Center, School of Chemical & Biotechnology, SASTRA Deemed to be University Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"},{"name":"Computational Biology lab, School of Chemical and Biotechnology, Thanjavur Tamil Nadu 613401 Shanmugha Arts Science Technology and Research Academy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"274","DOI":"10.12688\/wellcomeopenres.21289.1","article-title":"A predictive algorithm for the analysis of AMR trends and healthcare decision support [version 1; 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Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v3\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v3\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v3\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T06:29:08Z","timestamp":1785911348000},"score":1,"resource":{"primary":{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,17]]},"references-count":28,"URL":"https:\/\/doi.org\/10.12688\/wellcomeopenres.24103.3","relation":{"has-version":[{"id-type":"doi","id":"10.12688\/wellcomeopenres.24103.2","asserted-by":"subject"},{"id-type":"doi","id":"10.12688\/wellcomeopenres.24103.1","asserted-by":"subject"}],"has-review":[{"id-type":"doi","id":"10.21956\/wellcomeopenres.29978.r161300","asserted-by":"subject"},{"id-type":"doi","id":"10.21956\/wellcomeopenres.29978.r161300","asserted-by":"object"}]},"ISSN":["2398-502X"],"issn-type":[{"value":"2398-502X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,17]]},"assertion":[{"value":"Indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v3#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.21956\/wellcomeopenres.29978.r161300, Pownraj Brindangnanam, Pondicherry University, Kalapet, Pondicherry, 05 Aug 2026, version 3, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v3#referee-response-161300","order":0,"name":"referee-response-161300","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.26591.r136678, Hamad Abdel Hadi, Medical College, Qatar University, Doha, Doha, 13 Nov 2025, version 1, 2 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v1#referee-response-136678","order":1,"name":"referee-response-136678","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Suma Mohan<\/b>; \n<i>Posted: 17 Dec 2025<\/i>; Thank you very much for reviewing our manuscript entitled &quot;Exploring the evolution of multidrug resistance patterns in ESKAPEE pathogens using association mining: Key to antibiotic stewardship?&quot;. Thank you for this important observation. We agree that microbiology and infectious-disease expertise are essential for interpreting AMR surveillance outputs and ensuring that the associations examined are clinically meaningful. Our team primarily approached the problem from a data-science and bioinformatics perspective, and we acknowledge that the absence of direct infectious-disease\/microbiology expert involvement is a limitation of the current version of the work. However, we followed standard CLSI breakpoint guidelines, pathogen-specific recommended panels, and cross-checked associations against published literature to avoid biologically implausible interpretations. We appreciate the reviewer\u2019s suggestion and will incorporate microbiology domain expertise in the next iteration to refine pathogen-antibiotic pairing, add biological context filters, and improve clinical interpretability.","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v3#referee-comment-6357","order":2,"name":"referee-comment-6357","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.28259.r149736, Rasha Abdelsalam Elshenawy, University of Hertfordshire, Hatfield, England, 28 Mar 2026, version 2, 3 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v2#referee-response-149736","order":3,"name":"referee-response-149736","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Suma Mohan<\/b>; \n<i>Posted: 03 Jul 2026<\/i>; \n<b>Reviewers\u2019 response <\/b> We thank the reviewer for reviewing our manuscript entitled &quot;Exploring the evolution of multidrug resistance patterns in ESKAPEE pathogens using association mining: Key to antibiotic stewardship?&quot; and viewer response and for providing helpful comments. With this, we enclosed a point-by-point response to the reviewer\u2019s concerns for publication in your esteemed journal. We believe that we did our best to remove all the ambiguities and made changes that clearly improved our manuscript, which We have uploaded alongside this document. &nbsp;  \n<b>Clinical relevance of antibiotic-pathogen pairings (Reviewer 1, major concern) The authors acknowledged that microbiology\/infectious disease expertise was absent from the team and committed to incorporating it in future iterations. However, this limitation has not been addressed in the current version. Clinically implausible associations \u2014 such as meropenem resistance in&nbsp;<i>Enterococcus faecium<\/i>&nbsp;or&nbsp;<i>S. aureus<\/i>, where carbapenems are not clinically indicated \u2014 remain present in the association outputs without adequate biological contextualisation or filtering. Given that the stated aim is to support antibiotic stewardship, this is not a minor issue. At minimum, the authors should add a clearly visible disclaimer in both the manuscript and the dashboard itself, warning users that some rules may reflect testing artefacts or clinically irrelevant pairings, and recommending clinical review before acting on any rule.<\/b>  \n<b>Response: <\/b>We fully agree and thank the reviewer for this important observation and agree that certain antibiotic-pathogen associations identified through association rule mining may not necessarily correspond to clinically relevant treatment combinations. Since the analysis was performed only based on the available phenotypic susceptibility data in the Vivli data source, some association rules may reflect surveillance practices rather than true clinically associated relationships. We have now added a clearly visible disclaimer in the Results section of the manuscript as follows: \u201cAssociation rules identified in the current study may not necessarily correspond to clinically relevant treatment combinations. Because the analysis was performed on phenotypic susceptibility testing data available in the vivli data where some rules may reflect clinically irrelevant antibiotic-pathogen pairings. Thus, association rules should be interpreted as data-driven resistance co-occurrence patterns and it is strongly recommended to review by a clinician or infectious disease experts before acting on any rule generated by MERIT dashboard.\u201d We believe that the addition of this statement as disclaimer explicitly makes a meaningful and responsible step towards the usage of the dashboard outputs.  \n<b>False discovery and statistical rigour The authors acknowledged that no formal false discovery rate (FDR) correction was applied, and they committed to including this as a limitation. This is now stated in the text, which is acceptable for an exploratory study. However, the manuscript should be more explicit that the rules are hypothesis-generating only, and the abstract and conclusions should avoid language that implies clinical actionability without this caveat. The current conclusion \u2014 &quot;our findings from the data challenge could aid healthcare professionals in making informed decisions regarding antibiotic use&quot;, overstates the readiness of these findings for clinical application and should be tempered.<\/b>  \n<b>Response: <\/b>Thank you for this important constructive suggestion. We agree that, the identified association rules as hypothesis-generating rather than clinically validated findings. Accordingly, we revised the Abstract, Discussion and Conclusion sections of the manuscript and explicitly stated the following limitations. a) No actionable guidance when &quot;No rules found&quot;; (b) No active tab highlighting; (c) No sample-size warnings for sparse queries; (d) clinically implausible pairings not filtered; (e) no FDR correction; (f) No genomic validation; (g) Data access restrictions limiting exact reproducibility. &nbsp;  \n<b>Validation of sMDR patterns (Q9) The authors confirmed that no validation against genomic data, known resistance genes, or mobile genetic elements was performed. This is a significant limitation given that the central claim of the study is the identification of&nbsp;<i>shared<\/i>&nbsp;multidrug resistance patterns, a concept that inherently implies a shared mechanism. The authors acknowledge they cannot distinguish horizontal gene transfer from convergent evolution, which is appropriate, but the manuscript should more clearly frame all findings as phenotypic co-occurrence patterns rather than confirmed shared resistance mechanisms. The terminology &quot;sMDR&quot; throughout the manuscript implies mechanistic sharing, which is not supported by the data.<\/b>  \n<b>Response: <\/b>We thank the reviewer for this important observation. We respectfully clarify that the terminology &quot;shared multidrug resistance&quot; was adopted to describe multidrug resistance patterns identified through association rule mining of phenotypic antimicrobial susceptibility data, as used by Cazer et al. (2019)., We employed a similar approach to characterise shared resistance patterns among ESKAPEE pathogens using phenotypic MIC data, Apriori association rule mining, and network decomposition without genomic validation. Consistent with prior study, we use sMDR to denote co-occurrence patterns identified through association rule filtering and not a confirmed shared mechanism. However, we agree that the terminology horizontal gene transfer used in the Introduction part was not evaluated in the current study and overstates what phenotypic data alone can support. The identified associations should be interpreted as resistance co-occurrence patterns rather than direct evidence of shared molecular mechanisms. To improve clarity and avoid overinterpretation, we have revised the manuscript throughout to explicitly define sMDR as phenotypic multidrug resistance co-occurrence patterns identified through association rule mining. Additional text has been added to the Discussion and Limitations sections emphasizing that genomic validation was beyond the scope of the present study and that future analyses will be necessary to determine the biological mechanisms underlying the observed patterns.  \n<b>Threshold justification and sensitivity analysis (Q3)<\/b>  While the authors have now clarified that thresholds were chosen pragmatically based on dataset size, no formal sensitivity analysis was conducted. This is explicitly acknowledged, but a brief supplementary analysis showing how the number and strength of rules change under alternative threshold combinations (e.g., support = 0.05, 0.1, 0.2) for at least one representative organism would substantially strengthen confidence in the chosen parameters. The reviewer appreciates the authors' honesty here but encourages this as a minimal addition.  \n<b>Response: <\/b>We thank the reviewer and understand its scientific value. We agree that sensitivity analysis across alternative support thresholds would substantially strengthen the work by providing additional insight into the robustness of the identified association rules. We performed a targeted sensitivity analysis on the sMDR pattern presented in Figure 6. (meropenem, levofloxacin, ampicillin\/sulbactam, piperacillin\/tazobactam resistance), which is shared across \n<i>A. baumannii, K. pneumoniae<\/i>, and \n<i>E. faecium <\/i>by systematically varying support (0.1-0.5) and lift (&gt; 1.5 and &gt;2) thresholds while holding confidence and Zhang's metric fixed at the values used in the main analysis. The resulting resistance networks with varying thresholds for the three organisms are presented in Supplementary Figure S1. As expected, lower support thresholds generated a larger number of association rules, whereas higher thresholds retained fewer, stronger associations. For the specific sMDR pattern in Figure 6, we found that tightening the support and lift thresholds led to organism-specific degradation of the full four-drug pattern: Ampicillin\/sulbactam resistance was the most threshold-sensitive component, dropping out earliest in \n<i>A. baumannii<\/i> and \n<i>K. pneumoniae<\/i>, while piperacillin\/tazobactam and levofloxacin co-resistance association represents a comparatively robust shared signal across these three ESKAPEE pathogens. We have incorporated these findings into the Result part. We believe this targeted sensitivity analysis substantively addresses the reviewer's concern about threshold robustness. &nbsp;  \n<b>Dashboard usability limitations (Q13, Q14, Q15)<\/b>  The authors acknowledged several dashboard usability issues, including the absence of actionable guidance when &quot;No rules found&quot; is returned, the lack of active tab highlighting, and the absence of sample-size warnings for sparse country-specific queries, and stated these are planned for future upgrades. While it is understandable that not all enhancements can be implemented in this revision cycle, it would strengthen the manuscript if these limitations were clearly enumerated in a dedicated &quot;Limitations of the MERIT dashboard&quot; subsection, so that users of the current version are properly informed. \n<b>Response <\/b>: Thank you for this constructive suggestion. We have added a dedicated &quot;Limitations&quot; subsection to the manuscript to explicitly state the known limitations to the users of the MERIT dashboard. Limitations The identified sMDR patterns were derived from phenotypic antimicrobial susceptibility data and no genome analyses were performed. So, the observed associations should be inferred as phenotypic co-occurrence patterns rather than confirmed shared molecular resistance mechanisms. (B) No actionable guidance when &quot;No rules found&quot;; (C) No active tab highlighting is available in the dashboard; (D) No sample-size warnings for sparse queries; (E) clinically implausible pairings not filtered and requires experts\u2019 interpretation; (F) no FDR correction was applied; (G) Data access restrictions associated with the Vivli AMR datasets limit exact reproducibility of the reported results. We believe that this dedicated subsection enumerates the dashboard usability issues raised in Q13-Q15. &nbsp; &nbsp;  \n<b>Reproducibility under data-use restrictions (Q6)<\/b>  The code has been deposited on GitHub and Zenodo, which is positive. However, since the underlying data cannot be redistributed under the Vivli data-use agreement, independent replication of the specific results is not fully possible. The authors should explicitly acknowledge this in the manuscript under data availability or limitations, noting that the code can be applied to independently requested Vivli datasets but that exact result reproduction requires individual data access approval from Vivli. \n<b>Response: <\/b>Thank you for highlighting this. We have revised the Data Availability section and now added a dedicative \u201cReproducibility\u201d subsection to explicitly state that an independent data access approval from Vivli is recommended for exact reproduction of the results. We believe this directly addresses the reviewer's request and provides appropriate transparency regarding reproducibility. &nbsp;  \n<b>Temporal trend interpretation and breakpoint evolution (Q7)<\/b>  The authors confirmed that year-specific CLSI breakpoints were used and stated that specific yearly versions will be cited in the revised methods. However, these citations do not appear to have been added in the version 2 manuscript provided for review. Including these citations would be a straightforward but important step for transparency. &nbsp; \n<b>Response:<\/b> We apologise for this oversight. Phenotype assignment from MIC values using year-specific CLSI breakpoints were performed specifically for isolates from the 2014\u20132021 period where MIC data was available but R\/S\/I phenotype labels were missing; isolates from other years with missing phenotype labels were excluded rather than assigned using the corresponding annual CLSI breakpoint criteria. We have now added the specific CLSI yearly document citations in the Methods section under &quot;Dataset cleaning and wrangling. The corresponding MIC breakpoints for each organism across this period are provided in the newly added Supplementary Table S2. These references have been added to improve transparency and facilitate interpretation of temporal analyses. &nbsp; We are grateful to the reviewer for the thorough and expert evaluation, and for acknowledging the genuine contribution of the MERIT dashboard to AMR surveillance. We have addressed all major and minor points raised in this review. &nbsp; \n<b>Minor points<\/b> &nbsp;  \n<b>The abstract conclusion could be more carefully worded to reflect the exploratory nature of the work, particularly avoiding phrasing that implies direct clinical utility without validation.<\/b>  \n<b>Response:<\/b> We appreciate the reviewer\u2019s suggestion and have revised the Abstract conclusion as to better reflect the exploratory nature of the work and to remove any language implying direct clinical utility without validation. &nbsp;  \n<b>The paper's discussion of MERIT's unique value proposition relative to ECDC EARS-Net, Pfizer ATLAS portal, and WHO GLASS (Q16) has been addressed in the author response but does not appear to be substantively incorporated into the Discussion section of the revised manuscript. A short paragraph on this distinction would benefit readers.<\/b>  \n<b>Response: <\/b>We thank the reviewer for this recommendation. A new paragraph has been added to the Discussion section describing the relationship between MERIT and existing antimicrobial resistance surveillance platforms, including EARS-Net, ATLAS, and GLASS. The revised text highlights that MERIT is intended to complement existing surveillance resources by enabling exploratory association rule mining and resistance co-occurrence pattern discovery rather than replacing established surveillance systems. &nbsp;  \n<b>The authors note that ChatGPT-4 was used to improve readability, which is disclosed appropriately. Reviewers should be aware that some passages may benefit from further technical editing to ensure precision of language, particularly in the Methods and Discussion.<\/b>  &nbsp; \n<b>Response:<\/b> We thank the reviewer for this observation. The manuscript has undergone an additional round of technical editing to improve clarity, particularly within the Methods and Discussion sections.","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-461\/v3#referee-comment-6638","order":4,"name":"referee-comment-6638","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This work was supported by the Wellcome [227486 <a href=https:\/\/doi.org\/10.35802\/227486>https:\/\/doi.org\/10.35802\/227486<\/a>]; Department of Biotechnology, Ministry of Science and Technology, India [BT\/PR40144\/BTIS\/137\/46\/2022] [BT\/PR40150\/BTIS\/137\/81\/2023]. The funder had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":5,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}