{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T06:19:55Z","timestamp":1770358795532,"version":"3.49.0"},"reference-count":85,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"Abstract<\/jats:title>\n Background<\/jats:title>\n India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies.<\/jats:p>\n <\/jats:sec>\n Methods<\/jats:title>\n We compared the performance of a recently developed formula SCS$$_{BTT}$$<\/jats:tex-math>\n \n \n \n BTT<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden\u2019s Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures.<\/jats:p>\n <\/jats:sec>\n Results<\/jats:title>\n MCDM methods revealed that the Shine & Lal and SCS$$_{BTT}$$<\/jats:tex-math>\n \n \n \n BTT<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> were the best-performing formulae. Further, a modification of the SCS$$_{BTT}$$<\/jats:tex-math>\n \n \n \n BTT<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS$$_{BTT}$$<\/jats:tex-math>\n \n \n \n BTT<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> along with the condition MCV$$\\le$$<\/jats:tex-math>\n \u2264<\/mml:mo>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 80 fl was recommended for a higher heterogeneous population set. It was found that SCS$$_{BTT}$$<\/jats:tex-math>\n \n \n \n BTT<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> can classify all BTT samples with 100% sensitivity when MCV$$\\le$$<\/jats:tex-math>\n \u2264<\/mml:mo>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 80 fl.<\/jats:p>\n <\/jats:sec>\n Conclusions<\/jats:title>\n We addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS$$_{BTT}$$<\/jats:tex-math>\n \n \n \n BTT<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and its web application SUSOKA can provide 100% sensitivity when MCV$$\\le$$<\/jats:tex-math>\n \u2264<\/mml:mo>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 80 fl.<\/jats:p>\n <\/jats:sec>","DOI":"10.1186\/s12911-023-02388-w","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T12:03:40Z","timestamp":1704197020000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-criteria decision making to validate performance of RBC-based formulae to screen $$\\beta$$-thalassemia trait in heterogeneous haemoglobinopathies"],"prefix":"10.1186","volume":"24","author":[{"given":"Atul\u00a0Kumar","family":"Jain","sequence":"first","affiliation":[]},{"given":"Prashant","family":"Sharma","sequence":"additional","affiliation":[]},{"given":"Sarkaft","family":"Saleh","sequence":"additional","affiliation":[]},{"given":"Tuphan\u00a0Kanti","family":"Dolai","sequence":"additional","affiliation":[]},{"given":"Subhas\u00a0Chandra","family":"Saha","sequence":"additional","affiliation":[]},{"given":"Rashmi","family":"Bagga","sequence":"additional","affiliation":[]},{"given":"Alka\u00a0Rani","family":"Khadwal","sequence":"additional","affiliation":[]},{"given":"Amita","family":"Trehan","sequence":"additional","affiliation":[]},{"given":"Izabela","family":"Nielsen","sequence":"additional","affiliation":[]},{"given":"Anilava","family":"Kaviraj","sequence":"additional","affiliation":[]},{"given":"Reena","family":"Das","sequence":"additional","affiliation":[]},{"given":"Subrata","family":"Saha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,2]]},"reference":[{"key":"2388_CR1","doi-asserted-by":"publisher","unstructured":"Shrestha O, Khadwal AR, Singhal M, Trehan A, Bansal D, Jain R, Pal A, Hira JK, Chhabra S, Malhotra P, Das R, Sharma P. 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