Quantitative Weighting of Postoperative Complications Based on the Accordion Severity Grading System: Demonstration of Potential Impact Using the American College of Surgeons National Surgical Quality Improvement Program
Bibliographic record
Abstract
BACKGROUND: To quantify severity of postoperative complications based on the Accordion Severity Grading System, determine the ability of severity grading to enhance National Surgical Quality Improvement Program (NSQIP) data, and develop an aggregate measure of severity of complications (the postoperative morbidity index). STUDY DESIGN: Forty-three surgical experts rated case vignettes containing postoperative complications on a severity scale. Vignettes were based on the Accordion Severity Grading System derived from the Toronto Severity Grading System. The system was adjusted using the expert severity scale results and applied to 1 year of NSQIP outcomes (1,857 patients, 704 complications) at a large tertiary care center. RESULTS: Experts initially distinguished the 6 grades of severity in a highly significant manner (t-test probabilities all < 0.005), with 1 exception. They rated reoperation and single-system organ failure without reoperation as similar, rather than distinct, in severity. The Accordion System was adjusted to reflect this. Distinction of grades thereafter was highly significant (t-test probabilities all < 0.005). Application to American College of Surgeons NSQIP data provided important novel insights. For example, complications in 6 American College of Surgeons NSQIP categories spanned 4 or more severity grades. Severity-weighted outcomes revealed that quantitatively the greatest burden of outcomes was due to wound infection, shock, and return to the operating room, which is not revealed by unweighted outcomes. Based on this information, an aggregate measure of severity of complications-the postoperative morbidity index-was proposed. CONCLUSIONS: Quantitative severity weighting of complications is feasible. Adjustment of American College of Surgeons NSQIP outcomes using this quantitative severity grading system provides uniquely informative representations of relative burdens of 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 | 0.032 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".