Derivation and validation of a nomogram model for pulmonary thromboembolism in patients undergoing lung cancer surgery
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: A specific risk-stratification tool is needed to facilitate safe and cost-effective approaches to the prophylaxis of acute pulmonary thromboembolism (PTE) in lung cancer surgery patients. This study aimed to develop and validate a simple nomogram model for the prediction of PTE after lung cancer surgery using readily obtainable clinical characteristics. METHODS: A total of 14,427 consecutive adult patients who underwent lung cancer surgery between January 2015 and July 2018 in our institution were retrospectively reviewed. Included in the cohort were 136 patients who developed PTE and 544 non-PTE patients. The patients were randomly divided into the derivation group (70%, 95 PTE patients and 380 non-PTE patients) and the validation group (30%, 41 PTE patients and 164 non-PTE patients). A nomogram model was developed based on the results of multivariate logistic analysis in the derivation group. The cut-off values were defined using Youden's index. The prognostic accuracy was measured by area under the curve (AUC) values. RESULTS: In the derivation group, multivariate logistic analysis was carried out to evaluate the risk score. The risk assessment model contained five variables: age [95% confidence interval (CI): 1.008-1.083, P=0.016], body mass index (95% CI: 1.077-1.319, P=0.001), operation time (95% CI: 1.002-1.014, P=0.008), the serum level of cancer antigen 15-3 (CA15-3) before surgery (95% CI: 1.019-1.111, P=0.005), and the abnormal results of compression venous ultrasonography before surgery (95% CI: 2.819-18.838, P<0.001). All of them were independent risk factors of PTE. To simplify the risk assessment model, a nomogram model was established, which showed a good predictive performance in the derivation group (AUC 0.792, 95% CI: 0.734-0.853) and in the validation group (AUC 0.813, 95% CI: 0.737-0.890). CONCLUSIONS: A high-performance nomogram was established on the risk factors for PTE in patients undergoing lung cancer surgery. The nomogram could be used to provide an individual risk assessment and guide prophylaxis decisions for patients. Further external validation of the model is needed in lung cancer surgery patients in other clinical centers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it