Assessment of risk factors in the development of thromboembolism in a trauma population
Bibliographic record
Abstract
The aim of this study was to: (a) identify risk factors for the development of venothromboembolism in a trauma population, (b) evaluate whether risk factors vary with increasing Injury Severity Score (ISS), and (c) assess the predictive ability of the Risk Assessment Profile for Thromboembolism (RAPT) in this trauma population. There were 7532 admissions for trauma between 1993 and 1998 to the Montreal General Hospital trauma center. A nested case-control design was used. Cases were defined as all patients with radiological evidence of either a deep venous thrombosis or pulmonary embolus during their admission. Controls were patients satisfying the same inclusion criteria who did not suffer a symptomatic deep venous thrombosis or pulmonary embolism while in hospital, did not have evidence of deep venous thrombosis prior to the traumatic event, and found to be free of any symptomatic thromboembolic events on consequent follow up. Patients were divided into three categories, ISS 1--24 (mild-moderate injuries), ISS 25--59 (moderate-severe injuries), and ISS 60--75 (severe-fatal injuries). (Abstract shortened by UMI.)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".