Epidemiology and outcomes of missing admission medication history in severe trauma: A retrospective study
Bibliographic record
Abstract
OBJECTIVE: Anticoagulant and antiplatelet (ACAP) drugs are associated with increased mortality in trauma patients, therefore medication history on admission is important. Whether these medications are recorded on trauma admission has not been investigated, nor if absence of a medication history is associated with worse patient outcomes. METHODS: We conducted a retrospective database review combining demographic and outcome data from the St George Hospital (Sydney) trauma registry with admission medication history in the electronic record. To contrast medications with a known increased risk (ACAP) to patients with unknown risk, patients were divided into three groups: those on ACAPs, no-ACAP if medication history was present and no-ACAP documented, or no-Hx if no medication history recorded. Inclusion criteria were aged >16 and Injury Severity Score (ISS) >12. Admission demographic data and outcome data were compared between all three groups. RESULTS: Of 533 consecutive patients, 21% comprised the no-Hx group, while 22% were on an ACAP and 57% not on an ACAP. No-Hx patients had more severe head injuries and a younger median age compared to ACAP patients (42 vs 82 years old, P < 0.001). Mortality was higher for ACAP (24%; 95% CI 17-33%) compared to no-ACAP (11%; 95% CI 8-16%) or no-Hx patients (12%; 95% CI 7-20%) (P = 0.04). CONCLUSIONS: While a large number of severe trauma patients were admitted without a medication history, no-Hx patients did not appear at increased risk of adverse outcomes. ACAP patients had a higher mortality compared to no-ACAP highlighting the vulnerability of this group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".