An Appraisal of the Evidence Underlying Performance Measures for Community-acquired Pneumonia
Bibliographic record
Abstract
Numerous organizations use performance measures to monitor the quality of care provided for a variety of clinical conditions. An appraisal of the evidence underlying such performance measures has never been reported. Our objective was to estimate the effects of interventions recommended by performance measures and to determine the quality of evidence from which those estimates derive, using the Joint Commission and the Centers for Medicare and Medicaid Services' performance measures for community-acquired pneumonia (CAP) as examples. We performed systematic reviews of the literature to identify evidence related to the performance measures for CAP. Metaanalyses were then performed to estimate the absolute and relative effects of the interventions recommended by the performance measures. The Grading Recommendations, Assessment, Development, and Evaluation system was used to determine the quality of evidence. The estimated effects favored the interventions recommended by five of the six performance measures. These included pneumococcal vaccination (incidence of pneumococcal pneumonia: relative risk [RR], 0.43; 95% confidence interval [CI], 0.24-0.75), blood cultures, antibiotic administration within 6 hours, use of a guideline-compliant antibiotic regimen, and influenza vaccination (incidence of symptomatic influenza: RR, 0.30; 95% CI, 0.22-0.40). However, among these performance measures, only influenza vaccination was supported by high-quality evidence. One-step smoking cessation counseling was contradicted by moderate-quality evidence (smoking quit rate: RR, 1.05; 95% CI, 0.90-1.22). The evidence supporting performance measures is frequently not of high quality and occasionally contradictory.
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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 | 0.146 | 0.495 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".