Milestones in Surgical Complication Reporting
Bibliographic record
Abstract
OBJECTIVE: To provide improved guidance for the consistent application of the Clavien-Dindo classification (CDC) and Comprehensive Complication Index (CCI ® ) in challenging clinical scenarios. BACKGROUND: Standardized outcome reporting is key for the proper assessment of surgical procedures. A recent consensus conference recommended the CDC and the CCI ® for assessing postoperative morbidity. Several challenging scenarios for grading complications still require evidence-based guidance, and the use of the 2 metrics in randomized controlled trials (RCTs) remains unexplored. METHODS: We assessed the use of the CDC and CCI ® as an outcome measure in a systematic literature search. In addition, we asked 163 international surgeons to critically evaluate and independently grade complications in 20 complex clinical scenarios. Finally, a Core Group of 5 experts used this information to develop consistent recommendations. RESULTS: Until July 2023, 1327 RCTs selected the CDC and/or CCI ® to assess morbidity. Annual use was steadily increasing with now over 200 new RCTs per year. However, only a third (n = 335) of published RCTs provided the complete range of CDC grades, including all subgrades. Eighty-nine out of 163 surgeons (response rate: 55%) completed the questionnaire that served as a basis for the recommendations: repetitive interventions that are required to treat one complication, complications followed by further complications, complications occurring before referral, and expected and unrelated complications to the original procedure should all be counted separately and included in the CCI ® . Invasive blank diagnostic interventions should not be considered a complication. CONCLUSIONS: The increasing use of the CDC and CCI ® in RCTs highlights the importance of their standardized application. The current consensus on various difficult scenarios may offer novel guidance for the consistent use of the CDC and CCI ® , aiming to improve complication reporting and better quality control, ultimately benefiting all health care stakeholders and, first and foremost, all patients.
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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 | 0.494 | 0.709 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".