What Is the Best Way to Measure Surgical Quality? Comparing the American College of Surgeons National Surgical Quality Improvement Program versus Traditional Morbidity and Mortality Conferences
Bibliographic record
Abstract
BACKGROUND: Morbidity and mortality conferences have played a traditional role in tracking complications. Recently, the American College of Surgeons National Surgical Quality Improvement Program Pediatrics (ACS NSQIP-P) has gained popularity as a risk-adjusted means of addressing quality assurance. The purpose of this article is to report an analysis of the two methodologies used within pediatric plastic surgery to determine the best way to manage quality. METHODS: ACS NSQIP-P and morbidity and mortality data were extracted for 2012 and 2013 at a quaternary care institution. Overall complication rates were compared statistically, segregated by type and severity, followed by a subset comparison of ACS NSQIP-P-eligible cases only. Concordance and discordance rates between the two methodologies were determined. RESULTS: One thousand two hundred sixty-one operations were performed in the study period. Only 51.4 percent of cases were ACS NSQIP-P eligible. The overall complication rates of ACS NSQIP-P (6.62 percent) and morbidity and mortality conferences (6.11 percent) were similar (p = 0.662). Comparing for only ACS NSQIP-P-eligible cases also yielded a similar rate (6.62 percent versus 5.71 percent; p = 0.503). Although different complications are tracked, the concordance rate for morbidity and mortality and ACS NSQIP-P was 35.1 percent and 32.5 percent, respectively. CONCLUSIONS: The ACS NSQIP-P database is able to accurately track complication rates similarly to morbidity and mortality conferences, although it samples only half of all procedures. Although both systems offer value, limitations exist, such as differences in definitions and purpose. Because of the rigor of the ACS NSQIP-P, we recommend that it be expanded to include currently excluded cases and an extension of the study interval.
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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 | 0.100 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| 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".