Validation of Complications Selected by Consensus to Evaluate the Acute Phase of Adult Trauma Care
Bibliographic record
Abstract
OBJECTIVE: Evaluate the predictive validity of complications derived using expert consensus methodology to monitor the quality of trauma care. Secondary objectives were to assess the predictive validity of complications not selected by consensus and identify determinants of complications. BACKGROUND: A list of complications to monitor the quality of trauma care has recently been derived using Delphi consensus methodology. However, the predictive validity of consensus complications has not yet been demonstrated. METHODS: We conducted a multicenter cohort study of adults admitted to the 57 adult trauma centers of a Canadian integrated trauma system (2007-2012; n = 84,216). Multiple generalized linear models were used to assess the influence of complications on mortality and acute care length of stay (LOS) and to identify determinants of consensus complications. RESULTS: The presence of at least 1 consensus complication was associated with a 2.7-fold [95% confidence interval (CI): 2.45-2.90] and 2.2-fold (95% CI: 2.11-2.19) increase in the odds of mortality and mean LOS, respectively. Nonselected complications were associated with no increase in mortality (odds ratio = 0.90, 95% CI: 0.80-1.01) and a 60% increase in LOS (geometric mean ratio = 1.60, 95% CI: 1.57-1.62). Patient-related factors and factors related to treatment explained 66% and 34% of the variation in complication rates, respectively. CONCLUSIONS: In addition to the face and content validity ensured by consensus methodology, this study suggests that consensus complications have good predictive validity. Monitoring these complications as part of quality improvement activities would provide an opportunity to improve outcome and resource use for injury admissions.
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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 | 0.064 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".