Inter-rater variability for the American Society of Anesthesiologists classification in patients undergoing hepato-pancreato-biliary surgery (MILESTONE-2): international survey among surgeons and anaesthesiologists
Bibliographic record
Abstract
BACKGROUND: Patients undergoing hepato-pancreato-biliary surgery are typically preoperatively assessed using the American Society of Anesthesiologists (ASA) classification, which is also used for case-mix adjustment when comparing centre outcomes. Studies determining the inter-rater variability of the ASA classification within hepato-pancreato-biliary surgery are currently lacking. METHODS: An international survey was collected and a case-vignette study was performed (November 2022-April 2023) regarding the ASA classification in patients undergoing hepato-pancreato-biliary surgery among anaesthesiologists and surgeons from (inter)national societies. The survey consisted of 23 questions and eight case-vignettes. Primary analysis included descriptive statistics and the inter-rater variability was calculated using Light's Kappa. RESULTS: Overall, 1283 participants from 55 countries responded: 1073 (84%) anaesthesiologists and 210 (16%) surgeons. The ASA classification was commonly used, both clinically 1003/1283 (78%) and for research 728/762 (96%). The majority of respondents (n = 1019, 79%) declared that ASA score impacted their perioperative strategy. There inter-rater variability was fair-moderate (Kappa 0.26-0.42) in all case-vignettes. Inter-rater variability differed within and among geographic regions for each case. Over 80% (n = 1138) of respondents stated that they would take the underlying disease (for example cancer) into account, but this changed the preferred ASA score within the case-vignettes by only 1%. Type of surgery changed the preferred score in the case-vignettes (13% difference). The most common suggestions to improve the ASA classification were to clarify whether type of operation should be considered, create a more extensive definition, and provide more examples. CONCLUSIONS: Inter-rater variability was present within the ASA classification of patients undergoing hepato-pancreato-biliary surgery, which may impact perioperative strategy and hamper research results. Additional guidance to classify patients according to ASA is urgently needed. Until then, more objective measurements should be considered for case-mix adjustment within research.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".