Initial application in the EACTS and STS Congenital Heart Surgery Databases of an empirically derived methodology of complexity adjustment to evaluate surgical case mix and results
Bibliographic record
Abstract
OBJECTIVES: Outcomes evaluation is enhanced by assignment of operative procedures to appropriate categories based upon relative average risk. Formal risk modelling is challenging when a large number of operation types exist, including relatively rare procedures. Complexity stratification provides an alternative methodology. We report the initial application in the Congenital Heart Surgery Databases of the Society of Thoracic Surgeons (STS) and the European Association for Cardio-thoracic Surgery (EACTS) of an empirically derived system of complexity adjustment to evaluate surgical case mix and results. METHODS: Complexity stratification is a method of analysis in which the data are divided into relatively homogeneous groups (called strata). A complexity stratification tool named the STS-EACTS Congenital Heart Surgery Mortality Categories (STAT Mortality Categories) was previously developed based on the analysis of 77,294 operations entered in the Congenital Heart Surgery Databases of EACTS (33,360 operations) and STS (43,934 patients). Procedure-specific mortality rate estimates were calculated using a Bayesian model that adjusted for small denominators. Operations were sorted by increasing risk and grouped into five categories (the STAT Mortality Categories) that were designed to minimize within-category variation and maximize between-category variation. We report here the initial application of this methodology in the EACTS Congenital Heart Surgery Database (47,187 operations performed over 4 years: 2006-09) and the STS Congenital Heart Surgery Database (64,307 operations performed over 4 years: 2006-09). RESULTS: In the STS Congenital Heart Surgery Database, operations classified as STAT Mortality Categories 1-5 were (1): 17332, (2): 20114, (3): 9494, (4): 14525 and (5): 2842. Discharge mortality was (1): 0.54%, (2): 1.6%, (3): 2.4%, (4): 7.5% and (5): 17.8%. In the EACTS Congenital Heart Surgery Database, operations classified as STAT Mortality Categories 1-5 were (1): 19874, (2): 12196, (3): 5614, (4): 8287 and (5): 1216. Discharge mortality was (1): 0.99%, (2): 2.9%, (3): 5.0%, (4): 10.3% and (5): 25.0%. CONCLUSIONS: The STAT Mortality Categories facilitate analysis of outcomes across the wide spectrum of distinct congenital heart surgery operations including infrequently performed procedures.
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 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 | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".