The Comprehensive Complication Index
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a comprehensive complication index (CCI) that integrates all events with their respective severity. BACKGROUND: Reporting of surgical complications is inconsistent and often incomplete. Most studies fail to provide information about the severity of complications, or inform only on the most severe event, ignoring events of lesser severity. METHODS: We used an established classification of complications, adopting methods from operation risk index analysis in marketing research to develop a formula that considers all complications that may occur in a patient. The weights of each grade of complication, defined as median reference values, were obtained from 472 participants, who rated 30 different complications. Validation to assess sensitivity to treatment effects and validity of the CCI was performed by 4 different approaches, based on 1299 patients. RESULTS: The CCI is calculated as the sum of all complications that are weighted for their severity (multiplication of the median reference values from patients and physicians). The final formula yields a continuous scale to rank the severity of any combination of complications from 0 to 100 in a single patient. The CCI was highly sensitive in detecting treatment effect differences in the context of a randomized trial (effect size detected by CCI vs conventional standardized morbidity outcomes). It also showed a negative correlation with postoperative health status (r = -0.24, P = 0.002), and high correlation with the results of patient-rated single and multiple complications on conjoint analysis (r = 0.94, P < 0.001). CONCLUSIONS: The CCI summarizes all postoperative complications and is more sensitive than existing morbidity endpoints. It may serve as a standardized and widely applicable primary endpoint in surgical trials and other interventional fields of medicine. The CCI can be readily computed on the basis of tabulated complications according to the Clavien-Dindo classification (available at www.assessurgery.com).
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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 | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".