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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".