Paying the Price of Excluding Patients from a Trauma Registry
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to evaluate the impact of different trauma registry exclusion criteria on the assessment of trauma populations and outcome. METHODS: All patients admitted to a Canadian regional trauma center from April 1, 1993 to March 31, 2002 with a diagnosis of trauma (ICD-9 codes 800 to 959) were reviewed. TOTAL included everyone. REGISTRY included only patients meeting any of four criteria: death during hospital stay, transfer received from another hospital, admission to the intensive care unit, or hospital stay of 3 days or more. NOHIP excluded patients with isolated hip fracture. REG/NOHIP combined both. ISS12 and ISS15 excluded patients with ISS <12 and 15, respectively. RESULTS: There were 6,839 trauma patients. The percentage of excluded patients by group was: REGISTRY, 21.2%; NOHIP, 14.7%; REG/NOHIP, 34.9%; ISS12, 75%; and ISS15, 80.3%. Median length of stay was 7 days. Exclusions represented a total number of hospitalization days varying from 1.9% to 65.5% of TOTAL. Mortality was 6.9% for TOTAL, 8.6% for REGISTRY (p < 0.001), 5.7% for NOHIP (p = 0.009), 7.5% for REG/NOHIP (p=NS), 16.1% for ISS12 (p < 0.001), and 20.4% for ISS15 (p < 0.001). In groups with exclusions, transfer to long-term care varied from 0.14% to 23.5% in the excluded patients. For rehabilitation, these percentages varied from 0.14% to 17.6%. CONCLUSIONS: Registry exclusion criteria significantly alter the apparent severity of injury and resource utilization. The use of divergent exclusion criteria in the analysis of trauma registry data may be misleading.
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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 | 0.126 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".