Measuring Surgical Quality: Comparison of Postoperative Adverse Events with the American College of Surgeons NSQIP and the Thoracic Morbidity and Mortality Classification System
Bibliographic record
Abstract
BACKGROUND: Monitoring surgical outcomes is critical to quality improvement; however, different data-collection methodologies can provide divergent evaluations of surgical outcomes. We compared postoperative adverse event reporting on the same patients using 2 classification systems: the retrospectively recorded American College of Surgeons (ACS) NSQIP and the prospectively collected Thoracic Morbidity and Mortality (TM&M) system. STUDY DESIGN: Using the TM&M system, complications and deaths were documented daily by fellows and reviewed weekly by staff for all thoracic surgical cases conducted at our institution (April 1, 2010 to December 31, 2011). The ACS NSQIP recording was performed 30 to 120 days after index surgery by trained surgical clinical reviewers on a systemic sampling of major cases during the same time period. Univariate analyses of the data were performed. RESULTS: During the study period, 1,788 thoracic procedures were performed (1,091 were designated "major," as per ACS NSQIP inclusion criteria). The ACS NSQIP evaluated 182 of these procedures, representing 21.1% and 16.7% of patients and procedures, respectively. Mortality rates were 1.4% in TM&M vs 2.2% in ACS NSQIP (p = 0.42). Total patients and procedures with complications reported were 24.4% and 31.1% by TM&M vs 20.2% and 39.0% by ACS NSQIP (p = 0.23 and 0.03), respectively. Rates of reported cardiac complications were higher in TM&M vs ACS NSQIP (5.8% vs 1.1%; p = 0.01), and wound complications were lower (2.5% vs 6.0%; p = 0.01). CONCLUSIONS: Although overall rates were similar, significant differences in collection, definitions, and classification of postoperative adverse events were observed when comparing TM&M and ACS NSQIP. Although both systems offer complementary value, harmonization of definitions and severity classification would enhance quality-improvement programs.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".