Construct Validity and Reliability of the Comprehensive Complication Index as a Morbidity Outcome Measure in Pancreatic Surgery
Bibliographic record
Abstract
OBJECTIVE: To assess the reliability and construct validity of the CCI®️ following pancreatic surgery. SUMMARY BACKGROUND DATA: The Comprehensive Complication Index (CCI®️) is the only validated metric that quantifies cumulative morbidity, with a continuous score ranging from 0 (no complications) to 100 (death). METHODS: To address construct validity, we assessed patients undergoing elective pancreatic surgery for any disease at five Italian centers enrolled in a randomized controlled trial (NCT04438447) and a prospective cohort study (NCT04431076). The severity of 90-day complications was assessed using the CCI®️. We tested 10 a priori construct validity hypotheses through linear regression. Regression coefficients represented the between-group mean difference in CCI®️, with an effect size ≥0.2 considered potentially meaningful. Validity was deemed adequate if >75% of the hypotheses were supported. To address reliability, three independent raters among six centers assessed the CCI®️ from 100 anonymous case vignettes to evaluate inter-rater and inter-center reliability through intraclass correlation coefficient (ICC) and standard error of measurement (SEM). RESULTS: 797 patients were included (66±11 y, 50% female, 60% malignancy). The construct validity was supported by data, with 9/10 a priori hypotheses confirmed (90%). The CCI®️ showed excellent inter-rater (ICC=0.96, 95%CI: 0.95-0.97), high inter-center reliability (ICC >0.75 in each center), with a SEM ranging from 2.73 to 6.38. CONCLUSIONS: This study supports CCI®️as a valid and reliable measure of morbidity after pancreatic surgery, supporting its use in both clinical practice and comparative effectiveness research.
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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 | 0.033 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".