Multicentre Validation of a Prediction Score of Prolonged Air Leak for VATS Lobectomies
Bibliographic record
Abstract
Background: Prolonged air leak (PAL) >7days after VATS lobectomy (VL) increases length of stay and costs. Its incidence is between6–15%. PAL score (PS) was previously developed on extensive VL database using logistic model which retained 7 variables influencing PAL (Table 1a) Aims: Objective was to validate practical, user-friendly risk score to stratify PAL risks after VL to adopt early strategies for PAL/LOS reduction Methods: Multicentre VL retrospective database was analysed. Risk was calculated with PS(Table 1b). Area under ROC curve estimated discriminating value. Slope described relationship between predicted/observed PAL incidence. Hosmer–Lemeshow test estimated quality of adequacy between predicted/observed values Results: 266 VL analysed, PAL risks in Table1c. 46 (20.35%) experienced PAL. PS performances showed sensitivity 89.76%, specificity 89.36%, positive predictive value 97.35%, negative predictive value 66.67%, accuracy 89.68%. Repartition PS presents good predictive value with area under ROC 0.75 [CI 95%: 0.68–0.83] (Fig. 1). Slope and Hosmer–Lemeshow test show that probability predicted by PS has satisfactory adequacy with probability observed on sample Conclusions: External validation showed that PS is easy-to-use in PAL prediction. Primary application is to characterise population of subgroup analyses and to focus on preventing measures, for appropriate selection criteria in efficacy trial, minimising VL expense/risk
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 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".