Beyond P Values
Bibliographic record
Abstract
OBJECTIVE: To estimate the minimal important difference (MID) of the Comprehensive Complication Index (CCI ® ) in patients undergoing abdominal surgery. BACKGROUND: The CCI ® is a validated metric that quantifies cumulative surgical morbidity. While the CCI ® is a sensitive endpoint to detect treatment effects, a statistically significant effect does not necessarily translate into clinical relevance. Relevant differences from the patients' perspective are best captured by the MID. METHODS: Individual patient data were extracted from surgical studies reporting CCI ® at 30 days and using patient-reported outcome measures with established MIDs at baseline and 30 days. To determine the MID for the CCI ® , we used an anchor-based approach as recommended by methods guidelines. A patient-reported outcome measure was selected as an anchor only if the Spearman correlation coefficient between its change in score (baseline to 30 days postoperative) and the CCI ® was ≥|0.30|. We used linear regression to estimate the MID of the CCI ® across different anchors, and triangulation to determine a single MID. RESULTS: Data were extracted from 3 published randomized controlled trials and 1 prospective observational study (n = 1583 patients) in major abdominal surgery. In colorectal surgery cohorts, 2 subscores of the Short Form-36, 2 subscores of the Multidimensional Fatigue Inventory-20, the EuroQol-5-Dimension Index Score, and the EuroQol Visual Analog Scale showed a correlation with the CCI ® of ≥|0.30|. This resulted in MID estimates for the CCI ® ranging from 6.1 to 22.2. In hepato-pancreato-biliary surgery, 1 subscore of the Short Form-36, and 2 subscores of the Patient Reported Outcome Measure Information System-29 questionnaire qualified as anchors providing MID estimates ranging from 6.2 to 13.8. CONCLUSIONS: We propose a mean difference of 12 points in the CCI ® between treatment groups as a relevant difference in patients undergoing abdominal surgery. This MID provides an important foundation for sample size calculations and interpretation of randomized controlled trials and large real-world observational studies.
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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 | 0.075 | 0.370 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.093 | 0.016 |
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".