The Clavien-Dindo Classification of Surgical Complications
Bibliographic record
Abstract
In Brief Background and Aims: The lack of consensus on how to define and grade adverse postoperative events has greatly hampered the evaluation of surgical procedures. A new classification of complications, initiated in 1992, was updated 5 years ago. It is based on the type of therapy needed to correct the complication. The principle of the classification was to be simple, reproducible, flexible, and applicable irrespective of the cultural background. The aim of the current study was to critically evaluate this classification from the perspective of its use in the literature, by assessing interobserver variability in grading complex complication scenarios and to correlate the classification grades with patients', nurses', and doctors’ perception. Material and Methods: Reports from the literature using the classification system were systematically analyzed. Next, 11 scenarios illustrating difficult cases were prepared to develop a consensus on how to rank the various complications. Third, 7 centers from different continents, having routinely used the classification, independently assessed the 11 scenarios. An agreement analysis was performed to test the accuracy and reliability of the classification. Finally, the perception of the severity was tested in patients, nurses, and physicians by presenting 30 scenarios, each illustrating a specific grade of complication. Results: We noted a dramatic increase in the use of the classification in many fields of surgery. About half of the studies used the contracted form, whereas the rest used the full range of grading. Two-thirds of the publications avoided subjective terms such as minor or major complications. The study of 11 difficult cases among various centers revealed a high degree of agreement in identifying and ranking complications (89% agreement), and enabled a better definition of unclear situations. Each grade of complications significantly correlated with the perception by patients, nurses, and physicians (P < 0.05, Kruskal-Wallis test). Conclusions: This 5-year evaluation provides strong evidence that the classification is valid and applicable worldwide in many fields of surgery. No modification in the general principle of classification is warranted in view of the use in ongoing publications and trials. Subjective, inaccurate, or confusing terms such as “minor or major” should be removed from the surgical literature. A critical appraisal 5 years after introducing a new system to rank postoperative complications has shown a rapid and wide acceptance in the literature. To further support the use of this system, we documented an excellent correlation between the various grades of severity and perception by patients and health care providers, and obtained consensus among experts to rank difficult cases. The grades are sufficiently self explanatory that subjective terms such as minor and major complications should be avoided.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".