Thoracoscore and European Society Objective Score Fail to Predict Mortality in the UK
Bibliographic record
Abstract
BACKGROUND: Thoracoscore and the European Society Objective Score (ESOS.01) are two scoring systems used in thoracic surgery to estimate operative mortality risk. We aimed to evaluate if these are valid tools for use in the UK population. METHODS: A multi-center, prospective study was carried out on patients undergoing lung resection at six UK centers. Data were submitted electronically using our online data collection tool. Data were analyzed to determine the factors affecting mortality. A receiver operating characteristic analysis determined the ability of the thoracoscore and ESOS.01 to predict in-hospital mortality. RESULTS: Data were complete for 2,245 patients. The observed in-hospital mortality was 31 patients (1.38%). Mean thoracoscore was 2.66 (SD ± 3.21). Gender (P = 0.004, hazard ratio 4.786) and co-morbidity score (P = 0.005, hazard ratio 3.289) were identified as risk factors for mortality. A sub-analysis was performed using data from 1,912 patients with complete data for ESOS.01. In this group, mean thoracoscore was 2.55 (SD ± 2.94), mean ESOS.01 was 2.11(SD ± 1.41), and these were statistically significantly different (P < 0.0001). The observed in-hospital mortality was 28 patients (1.46%). The c-index for thoracoscore was 0.705, and for ESOS.01 was 0.739. CONCLUSIONS: Both thoracoscore and ESOS.01 overestimated mortality in the UK population. There is a continued need to develop an appropriate risk prediction system for the UK.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".