{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T11:45:35Z","timestamp":1791891935524,"version":"4.3.4"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Accident Analysis & Prevention"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1016\/j.aap.2024.107457","type":"journal-article","created":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T22:44:22Z","timestamp":1705790662000},"page":"107457","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":58,"special_numbering":"C","title":["Predicting pedestrian-involved crash severity using inception-v3 deep learning model"],"prefix":"10.1016","volume":"197","author":[{"given":"Md Nasim","family":"Khan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1671-2753","authenticated-orcid":false,"given":"Subasish","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinli","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aap.2024.107457_b0005","doi-asserted-by":"crossref","unstructured":"Abdelwahab, H.T., Abdel-Aty, M.A., 2001. Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections: 10.3141\/1746-02 1746 , 6\u201313. 10.3141\/1746-02.","DOI":"10.3141\/1746-02"},{"key":"10.1016\/j.aap.2024.107457_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107146","article-title":"A comprehensive study of child pedestrian crash outcomes in Ghana","volume":"189","author":"Adanu","year":"2023","journal-title":"Accid. Anal. Prev."},{"issue":"18","key":"10.1016\/j.aap.2024.107457_b0015","first-page":"1","article-title":"Gully erosion susceptibility assessment in the kondoran watershed using machine learning algorithms and the boruta feature selection","volume":"13","author":"Ahmadpour","year":"2021","journal-title":"Sustainability (switzerland)"},{"issue":"1","key":"10.1016\/j.aap.2024.107457_b0020","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/for.2425","article-title":"Severity Prediction of Traffic Accident Using an Artificial Neural Network","volume":"36","author":"Alkheder","year":"2017","journal-title":"J. Forecast."},{"issue":"5","key":"10.1016\/j.aap.2024.107457_b0025","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1080\/15389588.2023.2204386","article-title":"The urban structure and pedestrian injuries: A typological analysis of pedestrian crashes in the city of Hermosillo","volume":"24","author":"Armenta-Ramirez","year":"2023","journal-title":"Mexico. Traffic Injury Prevention"},{"key":"10.1016\/j.aap.2024.107457_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2020.105782","article-title":"Analysis of pedestrian-vehicle crash injury severity factors in Colorado 2006\u20132016","volume":"148","author":"Batouli","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2024.107457_b0035","first-page":"1","article-title":"A new spatial and flexible multivariate random-coefficients model for the analysis of pedestrian injury counts by severity level","volume":"16","author":"Bhat","year":"2017","journal-title":"Anal. Methods Acc. Res."},{"key":"10.1016\/j.aap.2024.107457_b0040","series-title":"Data Mining and Knowledge Discovery Handbook","first-page":"875","article-title":"Data Mining for Imbalanced Datasets: An Overview","author":"Chawla","year":"2009"},{"key":"10.1016\/j.aap.2024.107457_b0045","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"issue":"1","key":"10.1016\/j.aap.2024.107457_b0050","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13040-017-0155-3","article-title":"Ten quick tips for machine learning in computational biology","volume":"10","author":"Chicco","year":"2017","journal-title":"BioData Mining"},{"key":"10.1016\/j.aap.2024.107457_b0055","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/03611981221103229","article-title":"Deep learning approach for detecting lane change maneuvers using SHRP2 naturalistic driving data","volume":"2677","author":"Das","year":"2023","journal-title":"Transport. Res. Rec. J. Transport. Res. Board"},{"issue":"10","key":"10.1016\/j.aap.2024.107457_b0060","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1109\/LGRS.2019.2902675","article-title":"A combined deep learning model for the scene classification of high-resolution remote sensing image","volume":"16","author":"Dong","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"3","key":"10.1016\/j.aap.2024.107457_b0065","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/BF02289261","article-title":"Note on the \u201ccorrection for continuity\u201d in testing the significance of the difference between correlated proportions","volume":"13","author":"Edwards","year":"1948","journal-title":"Psychometrika"},{"key":"10.1016\/j.aap.2024.107457_b0070","first-page":"4","article-title":"An overview of classification algorithms for imbalanced datasets","volume":"2","author":"Ganganwar","year":"2012","journal-title":"Internat. J. Emerg. Technol. Adv. Eng."},{"key":"10.1016\/j.aap.2024.107457_b0075","unstructured":"Google, 2021. Imbalanced Data [WWW Document]. URL https:\/\/developers.google.com\/machine-learning\/data-prep\/construct\/sampling-splitting\/imbalanced-data (accessed 7.24.21)."},{"key":"10.1016\/j.aap.2024.107457_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2022.106937","article-title":"Factors affecting injury severity at pedestrian crossing locations with rectangular RAPID flashing Beacons (RRFB) using XGBoost and random parameters discrete outcome models","volume":"181","author":"Goswamy","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2024.107457_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssci.2022.105806","article-title":"Pedestrian-vehicle interaction severity level assessment at uncontrolled intersections using machine learning algorithms","volume":"153","author":"Govinda","year":"2022","journal-title":"Saf. Sci."},{"key":"10.1016\/j.aap.2024.107457_b0090","doi-asserted-by":"crossref","unstructured":"Guo, M., Yuan, Z., Janson, B., Yang, Y., 2020. A Two-Level Random Intercept Logit Model for Predicting Pedestrian-Vehicle Crash. p. pp 68-81. 10.1061\/9780784483152.007.","DOI":"10.1061\/9780784483152.007"},{"issue":"4","key":"10.1016\/j.aap.2024.107457_b0095","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1080\/15389588.2023.2183080","article-title":"Do factors associated with older pedestrian crash severity differ? A causal factor analysis based on exposure level of pedestrians","volume":"24","author":"Guo","year":"2023","journal-title":"Traffic Inj. Prev."},{"issue":"2","key":"10.1016\/j.aap.2024.107457_b0100","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.iatssr.2023.03.002","article-title":"Exploring association of contributing factors to pedestrian fatal and severe injury crashes under dark-no-streetlight condition","volume":"47","author":"Hossain","year":"2023","journal-title":"IATSS Research"},{"key":"10.1016\/j.aap.2024.107457_b0105","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jsr.2021.02.012","article-title":"Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model","volume":"77","author":"Hosseinzadeh","year":"2021","journal-title":"J. Saf. Res."},{"key":"10.1016\/j.aap.2024.107457_b0110","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.aap.2017.08.008","article-title":"Comparison of four statistical and machine learning methods for crash severity prediction","volume":"108","author":"Iranitalab","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2024.107457_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.jth.2022.101561","article-title":"An exploratory analysis of the effects of speed limits on pedestrian injury severities in vehicle-pedestrian crashes","volume":"28","author":"Islam","year":"2023","journal-title":"J. Transp. Health"},{"issue":"4","key":"10.1016\/j.aap.2024.107457_b0120","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1080\/17457300.2021.1928233","article-title":"Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study","volume":"28","author":"Jamal","year":"2021","journal-title":"Int. J. Inj. Contr. Saf. Promot."},{"key":"10.1016\/j.aap.2024.107457_b0125","series-title":"An Introduction to Statistical Learning: with Applications in R","author":"James","year":"2017"},{"key":"10.1016\/j.aap.2024.107457_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2020.105521","article-title":"Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data","volume":"142","author":"Khan","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2024.107457_b0135","unstructured":"Kitali, A.E., Kidando, E., Sando, T., Moses, R., Ozguven, E.E., 2018. Predicting the Likelihood of Aging Pedestrian Severe Crashes Using Dirichlet Random-Effect Bayesian Logistic Regression Model. p. 17p."},{"issue":"8","key":"10.1016\/j.aap.2024.107457_bib293","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1080\/19439962.2022.2129891","article-title":"A novel deep learning approach to predict crash severity in adverse weather on rural mountainous freeway","volume":"15","author":"Khan","year":"2023","journal-title":"Journal of Transportation Safety & Security"},{"issue":"2","key":"10.1016\/j.aap.2024.107457_bib291","article-title":"Prediction of Truck-Involved Crash Severity on a Rural Mountainous Freeway Using Transfer Learning with ResNet-50 Deep Neural Network","volume":"150","author":"Khan","year":"2024","journal-title":"Journal of Transportation Engineering, Part A: Systems"},{"issue":"5","key":"10.1016\/j.aap.2024.107457_b0140","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1177\/03611981221136138","article-title":"In-depth understanding of pedestrian-vehicle near-crash events at signalized intersections: an interpretable machine learning approach","volume":"2677","author":"Kong","year":"2023","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aap.2024.107457_b0145","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the Boruta package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"issue":"4","key":"10.1016\/j.aap.2024.107457_b0150","first-page":"271","article-title":"Boruta\u2013a system for feature selection","volume":"101","author":"Kursa","year":"2010","journal-title":"Fund. Inform."},{"issue":"4","key":"10.1016\/j.aap.2024.107457_b0155","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/17457300.2020.1791905","article-title":"Development of systemwide pedestrian safety performance function using stratified random sampling and a proxy measure of pedestrian exposure","volume":"27","author":"Kwayu","year":"2020","journal-title":"Int. J. Inj. Contr. Saf. Promot."},{"key":"10.1016\/j.aap.2024.107457_b0160","article-title":"Day-of-the-week variations and temporal instability of factors influencing pedestrian injury severity in pedestrian-vehicle crashes: a random parameters logit approach with heterogeneity in means and variances","volume":"29","author":"Li","year":"2021","journal-title":"Anal. Methods Acc. Res."},{"issue":"2","key":"10.1016\/j.aap.2024.107457_b0165","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"issue":"3","key":"10.1016\/j.aap.2024.107457_b0170","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s42979-023-01695-3","article-title":"Image-based sentiment analysis using InceptionV3 transfer learning approach","volume":"4","author":"Meena","year":"2023","journal-title":"SN Computer Science"},{"issue":"2","key":"10.1016\/j.aap.2024.107457_b0175","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ijtst.2020.01.001","article-title":"Factors affecting injury severity in vehicle-pedestrian crashes: A day-of-week analysis using random parameter ordered response models and Artificial Neural Networks","volume":"9","author":"Mokhtarimousavi","year":"2020","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"10.1016\/j.aap.2024.107457_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106240","article-title":"Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling","volume":"159","author":"Morris","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2024.107457_b0185","unstructured":"NHTSA, 2021. Traffic Safety Fact (2021 Data): Pedestrian (No. DOT HS 813 458). National Highway Traffic Safety Administration, Washington, DC."},{"key":"10.1016\/j.aap.2024.107457_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2022.106610","article-title":"Analysis of pedestrian accident injury-severities at road junctions and crossings using an advanced random parameter modelling framework: the case of Scotland","volume":"169","author":"Olowosegun","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2024.107457_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106090","article-title":"A deep learning based traffic crash severity prediction framework","volume":"154","author":"Rahim","year":"2021","journal-title":"Acc. Anal. Prevent."},{"key":"10.1016\/j.aap.2024.107457_b0200","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.procs.2019.11.147","article-title":"Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3","volume":"161","author":"Sam","year":"2019","journal-title":"Proc. Comput. Sci."},{"issue":"1","key":"10.1016\/j.aap.2024.107457_b0205","first-page":"1","article-title":"DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture","volume":"9","author":"Sharma","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aap.2024.107457_b0210","article-title":"Modeling pedestrian-injury severities in pedestrian-vehicle crashes considering spatiotemporal patterns: insights from different hierarchical bayesian random-effects models","volume":"28","author":"Song","year":"2020","journal-title":"Anal. Methods Acc. Res."},{"key":"10.1016\/j.aap.2024.107457_b0215","series-title":"Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.aap.2024.107457_b0220","series-title":"Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"issue":"3","key":"10.1016\/j.aap.2024.107457_b0225","first-page":"273","article-title":"Modelling pedestrian crash severity at mid-blocks","volume":"13","author":"Toran Pour","year":"2017","journal-title":"Transportmet. A: Transport Sci."},{"key":"10.1016\/j.aap.2024.107457_b0230","doi-asserted-by":"crossref","first-page":"98","DOI":"10.3141\/2237-11","article-title":"Random parameter model used to explain effects of built-environment characteristics on pedestrian crash frequency","volume":"2237","author":"Ukkusuri","year":"2011","journal-title":"Transport. Res. Rec. J. Transport. Res. Board"},{"key":"10.1016\/j.aap.2024.107457_b0235","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"10.1016\/j.aap.2024.107457_b0240","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1080\/19439962.2018.1551257","article-title":"Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian-motor vehicle crashes at signalized crossings","volume":"12","author":"Wang","year":"2020","journal-title":"J. Transport. Safety Security"},{"issue":"9","key":"10.1016\/j.aap.2024.107457_b0245","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1177\/0361198119844456","article-title":"Prediction and factor identification for crash severity: Comparison of discrete choice and tree-based models","volume":"2673","author":"Wang","year":"2019","journal-title":"Transport. Res. Rec. J. Transport. Res. Board"},{"issue":"6","key":"10.1016\/j.aap.2024.107457_b0250","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1080\/01441647.2021.1954108","article-title":"Applications of machine learning methods in traffic crash severity modelling: current status and future directions","volume":"41","author":"Wen","year":"2021","journal-title":"Transp. Rev."},{"key":"10.1016\/j.aap.2024.107457_b0255","unstructured":"WHO, 2023. Road traffic injuries [WWW Document]. URL https:\/\/www.who.int\/health-topics\/road-safety (accessed 8.3.23)."},{"key":"10.1016\/j.aap.2024.107457_b0260","article-title":"Modeling pedestrian injury severity: A case study of using extreme gradient boosting vs random forest in feature selection","author":"Wu","year":"2023","journal-title":"Transport. Res. Rec. J. Transport. Res. Board"},{"issue":"2","key":"10.1016\/j.aap.2024.107457_b0265","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1080\/15389588.2022.2156789","article-title":"Modeling pedestrian injury severity in pedestrian-vehicle crashes considering different land use patterns: Mixed logit approach","volume":"24","author":"Yang","year":"2023","journal-title":"Traffic Inj. Prev."},{"key":"10.1016\/j.aap.2024.107457_b0270","article-title":"Temporal stability of pedestrian injury severity in pedestrian-vehicle crashes: New insights from random parameter logit model with heterogeneity in means and variances","volume":"32","author":"Zamani","year":"2021","journal-title":"Anal. Methods Acc. Res."},{"key":"10.1016\/j.aap.2024.107457_b0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.107119","article-title":"Analysis of the injury severity of motor vehicle\u2013pedestrian crashes at urban intersections using spatiotemporal logistic regression models","volume":"189","author":"Zeng","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2024.107457_b0280","doi-asserted-by":"crossref","first-page":"60079","DOI":"10.1109\/ACCESS.2018.2874979","article-title":"Comparing prediction performance for crash injury severity among various machine learning and statistical methods","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.aap.2024.107457_b0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2023.106964","article-title":"Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm","volume":"182","author":"Zhu","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.aap.2024.107457_b0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssci.2021.105479","article-title":"Deep learning for autonomous vehicle and pedestrian interaction safety","volume":"145","author":"Zhu","year":"2022","journal-title":"Saf. Sci."}],"container-title":["Accident Analysis & Prevention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457524000022?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0001457524000022?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T23:41:21Z","timestamp":1757547681000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0001457524000022"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":60,"alternative-id":["S0001457524000022"],"URL":"https:\/\/doi.org\/10.1016\/j.aap.2024.107457","relation":{},"ISSN":["0001-4575"],"issn-type":[{"value":"0001-4575","type":"print"}],"subject":[],"published":{"date-parts":[[2024,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting pedestrian-involved crash severity using inception-v3 deep learning model","name":"articletitle","label":"Article Title"},{"value":"Accident Analysis & Prevention","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aap.2024.107457","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107457"}}