Hips Can Lie: Impact of Excluding Isolated Hip Fractures on External Benchmarking of Trauma Center Performance
Bibliographic record
Abstract
BACKGROUND: Trauma centers (TCs) vary in the inclusion of patients with isolated hip fractures (IHFs) in their registries. This inconsistent case ascertainment may have significant implications on the assessment of TC performance and external benchmarking efforts. METHODS: Data were derived from the National Trauma Data Bank (2007-8.1). We included patients (aged 16 years or older) with Injury Severity Score value ≥ 9 who were admitted to Level I and II TCs. To ensure data quality, we limited the study to TC that routinely reported comorbidities and Abbreviated Injury Scale codes. IHF were defined as patients, aged 65 years or older, injured as a result of falls, with Abbreviated Injury Scale codes for hip fracture and without other significant injuries. TCs were stratified according to their reported inclusion of IHF in their registry. Observed-to-expected mortality ratios were used to rank TC performance first with and then, without the inclusion of patients with IHF. RESULTS: In total, 91,152 patients in 132 TCs were identified; 5% (n = 4,448) were IHF. The proportion of IHF per TC varied significantly, ranging from 0% to 31%. When risk-adjusted mortality was evaluated, excluding patients with IHF had significant effects: 37% (n = 49) of TCs changed their performance rank by ≥ 3 (range, 1-25) and 12% of centers changed their performance quintile. The greatest change in rank performance was evident in centers that routinely include IHF in their registries. CONCLUSIONS: Given the fact that IHFs in the elderly significantly influence risk-adjusted outcomes and are variably reported by TCs, these patients should be excluded from subsequent benchmarking efforts.
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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 | 0.001 |
| 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".