Lack of Emergency Medical Services Documentation Is Associated with Poor Patient Outcomes: A Validation of Audit Filters for Prehospital Trauma Care
Bibliographic record
Abstract
BACKGROUND: Our previous Delphi study identified several audit filters considered sensitive to deviations in prehospital trauma care and potentially useful in conducting performance improvement, a process currently recommended by the American College of Surgeons Committee on Trauma. This study validates 2 of those proposed audit filters. STUDY DESIGN: We studied 4,744 trauma patients using the electronic records of the Central Region Trauma registry and Emergency Medical Services (EMS) patient logs for the period January 1, 2002, to December 31, 2004. We studied whether requests by on-scene Basic Life Support (BLS) for Advanced Life Support (ALS) assistance or failure by EMS personnel to record basic patient physiology at the scene was associated with increased in-hospital mortality. We performed multivariate analyses, including a propensity score quintile approach, adjusting for differences in case mix and clustering by hospital. RESULTS: Overall mortality was 6.1%. A total of 28.2% (n = 1,337) of EMS records were missing patient scene physiologic data. Multivariate analysis revealed that patients missing 1 or more measures of patient physiology at the scene had increased risk of death (adjusted odds ratio = 2.15; 95% CI, 1.13 to 4.10). In 17.4% (n = 402) of cases BLS requested ALS assistance. Patients for whom BLS requested ALS had a similar risk of death as patients for whom ALS was initially dispatched (odds ratio = 1.04; 95% CI, 0.51 to 2.15). CONCLUSIONS: Failure of EMS to document basic measures of scene physiology is associated with increased mortality. This deviation in care can serve as a sensitive audit filter for performance improvement. The need by BLS for ALS assistance was not associated with increased mortality.
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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 | 0.136 | 0.266 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".