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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".