Benchmarking trauma center performance in traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: Trauma centers (TCs) generally use mortality to gauge performance. However, differences in mortality outcomes might reflect different approaches or philosophies toward end-of-life care. We postulate that discharge home (DH) as a proxy for functional outcome may be a more useful measure of quality and may have significant implications on the assessment of TC performance and external benchmarking efforts. METHODS: Data were derived from the National Trauma Data Bank (2007-2009). We included patients (18 years or older) with isolated, severe blunt head injuries who were admitted to Level I and Level II TCs. Observed-to-expected (O/E) mortality ratios were calculated and used to rank TC performance by mortality and then DH. Concordance between performance measures was calculated using a weighted kappa statistic. RESULTS: In total, 19,705 patients in 240 TCs were identified. Crude mortality ranged from 4% to 60%, whereas rates of DH ranged from 3% to 66%. When O/E ratios for mortality were evaluated, five centers were identified as high performers. Of these five centers, only two were also high performers for DH. The concordance of outlier status and correlation across O/E ratios between mortality and DH high performers was 0.16 (poor). CONCLUSION: Centers that are characterized as high performers when evaluating mortality are not high performers for functional outcome as evaluated by DH. DH may provide an alternative way of assessing quality of care delivered to patients with traumatic brain injury. LEVEL OF EVIDENCE: Care management study, level III.
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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 | 0.038 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".