Change in Adverse Events After Enrollment in the National Surgical Quality Improvement Program: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The American College of Surgeons' National Surgical Quality Improvement Program (NSQIP) is the first nationally validated, risk-adjusted, outcomes-based program to measure and compare the quality of surgical care across North America. Participation in this program may provide an opportunity to reduce the incidence of adverse events related to surgery. STUDY DESIGN: A systematic review of the literature was performed. MedLine, EMBASE and PubMed were searched for studies relevant to NSQIP. Patient characteristics, intervention, and primary outcome measures were abstracted. The intervention was participation in NSQIP and monitoring of Individual Site Summary Reports with or without implementation of a quality improvement program. The outcomes of interest were change in peri-operative adverse events and mortality represented by pooled risk ratios (pRR) and 95% confidence intervals (CI). RESULTS: Eleven articles reporting on 35 health care institutions were included. Nine (82%) of the eleven studies implemented a quality improvement program. Minimal improvements in superficial (pRR 0.81; 95% CI 0.72-0.91), deep (pRR 0.82; 95% CI0.64-1.05) and organ space (pRR 1.15; 95% CI 0.96-1.37) infections were observed at centers that did not institute a quality improvement program. However, centers that reported formal interventions for the prevention and treatment of infections observed substantial improvements (superficial pRR 0.55, 95% CI 0.39-0.77; deep pRR 0.61, 95% CI 0.50-0.75, and organ space pRR 0.60, 95% CI 0.50-0.71). Studies evaluating other adverse events noted decreased incidence following NSQIP participation and implementation of a formal quality improvement program. CONCLUSIONS: These data suggest that NSQIP is effective in reducing surgical morbidity. Improvement in surgical quality appears to be more marked at centers that implemented a formal quality improvement program directed at the reduction of specific morbidities.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".