Temporal Validation and Extension of a Risk Prediction Model for Postoperative Pulmonary Complications in Head and Neck Surgery Patients
Bibliographic record
Abstract
Background : Postoperative pulmonary complications (PPCs) are common following head and neck surgeries (HNS) leading to longer hospital stays and significant morbidity and mortality. Current PPC risk prediction models developed for general surgery patients underperform for HNS. This study validates an existing model and extends it for HNS patients. Methods : Using the NSQIP-ACS database (2018-19), we validated the Gupta model, developed on the same database (2007-08). Later, we recalibrated and updated the model with additional predictors relevant for HNS, followed by internal-external validation. The model performance was evaluated by scaled Brier score and Nagelkerke’s R 2 . The discrimination ability was measured by C-statistic (AUC) and calibration was assessed by calibration slope. Results : After extension and validation, the updated model achieved improved performance, with a Brier score of 0.0234 and R 2 of 0.1435. C-statistic rose to 0.822 (95% CI: 0.785–0.858), and the calibration slope increased to 0.979. Conclusion : The updated model showed better performance, discrimination, and calibration in predicting PPC in HNS patients compared to the original model.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".