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Conventional statistical methods often fail to separate this severity-dependent selection from actual outcomes. We applied a \u201cdoubly robust\u201d statistical framework (augmented inverse probability weighting, AIPW), which combines a treatment model and an outcome model, to better account for measured differences, reduce selection bias, and examine whether on-scene time modifies this association.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a nationwide retrospective cohort study using emergency medical service transport records from the Fire and Disaster Management Agency of Japan (January 2021\u2013December 2023). Of 13,821,224 records, 143,130 patients from traffic-related multi-patient incidents (TMIs; \u22652 patients per incident) with complete data were included. The exposure was priority transport. The primary outcome was physician-assessed death or critical\/severe injury at hospital arrival. Marginal adjusted risk differences (RDs) were estimated using AIPW. Effect modification by on-scene time (5\u201360\u00a0min) was assessed by marginal standardization with cluster-bootstrap confidence intervals. Sensitivity analyses included overlap restriction and E-value quantification of unmeasured confounding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 143,130 patients (median age 41 years; female, 50.57%), 62,769 (43.85%) underwent priority transport. Severe outcomes occurred in 4.35% of priority and 1.87% of non-priority patients. The overall RDs was 2.2% points (95% CI, 2.01\u20132.36). A significant interaction with on-scene time was observed (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; .001): the marginal RD was peaked at 5\u00a0min (6.4% points; 95% CI, 5.6\u20137.2), declined to a minimum of 1.4% points (95% CI, 1.3\u20131.6) at approximately 32\u00a0min, and increased modestly to 2.4% points (95% CI, 2.0\u20132.8) at 60\u00a0min. Sensitivity analyses confirmed result robustness, yielding an E-value of 3.76 (lower bound, 3.57).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The association between priority transport and severe outcome was modified by on-scene time, with the largest risk difference at the shortest on-scene times. This U-shaped pattern is consistent with preferential selection of more severely injured patients for rapid transport, rather than a direct effect of transport order. Accounting for prehospital triage selection when interpreting transport-outcome associations in critical care research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13054-026-06196-2","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T09:38:03Z","timestamp":1785836283000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prehospital transport prioritization and time-varying selection bias in traffic-related multi-patient incidents: a nationwide retrospective cohort study"],"prefix":"10.1186","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0141-1285","authenticated-orcid":false,"given":"Atsushi","family":"Kubo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinobu","family":"Tamura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsushi","family":"Hiraide","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daigo","family":"Morioka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryu","family":"Murakami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenko","family":"Fukui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shigeaki","family":"Inoue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"issue":"1","key":"6196_CR1","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1186\/s13049-021-00932-z","volume":"29","author":"A Khorram-Manesh","year":"2021","unstructured":"Khorram-Manesh A, Nordling J, Carlstr\u00f6m E, Goniewicz K, Faccincani R, Burkle FM. 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Registry data use was authorized by the Fire and Disaster Management Agency (FDMA) of Japan.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"524"}}