Clavien classification in urology: Is there concordance among post-graduate trainees and attending urologists?
Bibliographic record
Abstract
PURPOSE: We assess the variations between post-graduate trainees (PGTs) and attending urologists in applying the Revised Clavien-Dindo Classification System (RCCS) to urological complications. METHODS: Twenty postoperative complications were selected from urology service Quality Assurance meeting minutes spanning 1 year at a tertiary care centre. The cases were from adult and pediatric sites and included minor and major complications. After a briefing session to review the RCCS, the survey was administered to 16 attending urologists and 16 PGTs. Concordance rates between the two groups were calculated for each case and for the whole survey. Inter-rater agreement was calculated by kappa statistics. RESULTS: There was good overall agreement rate of 81 % (range: 30-100) when both groups were compared. Thirteen of the 20 cases (65%) held an agreement rate above 80% (k = 0.753, p < 0.001) including 3 (15%) cases with 100% agreement. There were only 2 cases where the scores given by PGTs were significantly different from that given by attending urologists (p ≤ 0.03). There was no significant difference between both groups in terms of overall RCCS grades (p = 0.12). When all participants were compared as one group, there was good overall inter-rater agreement rate of 75% (k = 0.71). Although the percent of overall agreement rate among PGTs was higher than the attending urologists (82% [k = 0.79] vs. 69% [k = 0.64]), this was not significantly different (p = 0.68). CONCLUSION: There was good overall agreement among PGTs and attending urologists in application of the RCCS in urology. Therefore, it is appropriate for PGTs to complete the Quality Assurance meeting reports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".