P-096RISK-ADJUSTED COMPARISON OF PERFORMANCE BETWEEN THREE ACADEMIC THORACIC SURGERY UNITS USING THE EUROLUNG RISK MODELS
Bibliographic record
Abstract
Objectives: To compare the performance of 3 thoracic surgery centres using the Eurolung risk models for morbidity and mortality. Methods: Retrospective analysis on prospective databases from 3 academic centres (2014-2016). Two thousand eleven patients (721 patients from centre 1 857 from centre 2 and 433 from centre 3) undergoing anatomic lung resections (1640 lobectomies, 227 segmentectomies and 144 pneumonectomies, 63% by minimally invasive techniques) were analysed. The Eurolung1 and Eurolung2 models were used to risk-adjust cardiopulmonary morbidity and 30-day mortality rates. ANOVA and Student’s t-test were used to compare average outcomes between and within centres. Results: Overall cardiopulmonary complication and 30-day mortality rates were 25% and 2.08%. Analysis of morbidity: The observed morbidity rate of centre 3 (41%) was significantly higher than the ones of centre 1 (21.2%, P < 0.0001) and 2 (20.2%, P < 0.0001). The observed morbidity of centre 1 was in line with the predicted one (22.7% vs 21.1%, P=0.3). Centre 2 performed better than expected (observed morbidity 20.2% vs predicted 26.7%, P < 0.0001), whereas the observed morbidity of centre 3 was higher than the predicted one (41.1% vs 24.3%, P < 0.0001). Analysis of mortality: The observed mortality of centre 1 (3.6%) was higher than those of centres 2 (1.2%, P=0.001) and 3 (1.4%, P=0.03). The mortality rate observed in centre 1 was in line with the predicted one (3.6% vs 4.3, P=0.3), whereas centres 2 (1.2% vs 5.2%, P < 0.0001) and 3 (1.4% vs 5%, P < 0.0001) had observed mortality rates significantly lower than the predicted ones. The mortality rates observed in those patients with major cardiopulmonary complications (according to the TMM score >2) were 32% in centre 1 (vs predicted mortality 8%, P=0.0001), 8.2% in centre 2 (vs predicted mortality 8.1%, P=0.9) and 9% in centre 3 vs predicted mortality 7.1%, P=0.7). Conclusions: The use of Eurolung models allow for an objective comparison of performance between different centres by reliably identifying outcome differences. Our analysis should be interpreted as a methodological template for future quality improvement initiatives. Disclosure: No significant relationships.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".