Development of a standardised evaluation grid for trauma center accreditation process
Bibliographic record
Abstract
Regionalised and integrated trauma systems are effective in reducing mortality. Following initial designation of 59 trauma centres in the province of Quebec, Canada, each centre was evaluated in situ twice over a period of 10 years by an external panel of experts according to American College of Surgeons criteria. Problem Reports do not reflect a systematic approach as they are focusing on major strengths and flaws. This hinders their usefulness in measuring performance. Objective Test the reliability and validity of a standardised evaluation grid. Methods A retrospective review of 115 accreditation reports (three were missing) on 59 designated trauma centres was performed. A systematic grid containing 84 elements grouped under three themes (commitment, program and procedures) was applied to all reports by a single reviewer to calculate performance scores. Two analyses were performed: (1) a random sample of 12 reports was duplicated to measure intra-rater agreement, and (2) content validity was assessed by comparing median scores of the 115 reports by accreditation results (accreditation positive, revisit required and designation postponement). Results The scoring system demonstrated excellent intra-rater agreement as 76 of the 84 elements showed strong or close to perfect agreement (weighted κ>0.60). Median scores decreased significantly when a revisit was requested or postponement of accreditation occurred (Spearman's rho=–0.71, p<0.0001). Conclusion A more systematic approach is required for trauma centre accreditation site visits. An evaluation grid may help by providing a more reliable and valid assessment of their performance.
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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 | 0.252 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".