Prevalence of the Clavien Dindo Classification in the Reporting of Surgical Complications in Major Urological Journals
Bibliographic record
Abstract
ObjectivesTo review the methods of surgical complication reporting in urological journals, to determine the current utilisation of the Clavien Dindo classification, and to make comparison with previous reports over the last 10 years.MethodsA search was performed of all journal articles published in 5 major urological journals from January 2021 to April 2022, inclusive. All articles reporting surgical outcomes or complications were analysed. The current trend in complication reporting was compared with the results of the systematic search of the same 5 urological journals performed in 2012 by Yoon et al.ResultsA total of 137 articles were identified. The Clavien Dindo classification was the most common method used (105/137, 76.6%) followed by a text-based descriptive classification (31/137, 22.6%). Notably, the Clavien Dindo classification was the only standardised method used in any of the articles examined. The prevalence of Clavien Dindo classification usage is 76.6% in the articles analysed in our search compared with the 33.3% reported by Yoon et al. in their search of papers published in the same 5 urological journals between 2010 and 2012.ConclusionsThere has been a significant increase in the adoption of the Clavien Dindo classification in the reporting of complications in major urological journals in the last decade. This is a favourable trend which is likely in response to the ad hoc EAU Guidelines Panel 2012 recommendations. With more than 20% of journal articles still using descriptive text-based classifications, we should continue to encourage further implementation of standardised criteria, particularly the Clavien Dindo classification
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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 | 0.062 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.041 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".