Estimating the Risk of Prolonged Air Leak after Pulmonary Resection Using a Simple Scoring System
Bibliographic record
Abstract
BACKGROUND: The high rate of prolonged air leak (PAL) after pulmonary resection has prompted interest in surgical adjuncts designed to prevent this complication. However, these adjuncts are costly and might not be beneficial if used routinely. Identification of patients at highest risk might allow for more effective use of these adjuncts. Therefore, we sought to develop a simple scoring system to predict PAL. STUDY DESIGN: A derivation set of 580 patients was identified from a prospectively entered database of consecutive pulmonary resections at a single institution from 2002 to 2007. Patient and operative characteristics were compared using Student's t-test and chi-square tests. Significant variables on univariate analysis were entered into a stepwise logistic regression to establish a simple predictive model to estimate the risk of PAL. This scoring system was then validated in a consecutive set of 381 patients operated at the same institution from 2007 to 2009. RESULTS: The rate of PAL was 14% in the derivation set and 18% in the validation set. Poor pulmonary function (forced expiratory volume in 1 second and carbon monoxide diffusing capacity, percent predicted) and pleural adhesions were significantly associated with PAL in the derivation set. A weighted scoring system was devised using pleural adhesions (+2 points), forced expiratory volume in 1 second (+1 per 10% below 100%), and carbon monoxide diffusing capacity (+1 per 20% below 100%). Total number of points estimated the probability of PAL. Hosmer-Lemeshow goodness-of-fit test confirmed validity (p > 0.2) of this scoring system in the validation set. CONCLUSIONS: We have devised and validated a simple scoring system to predict the probability of PAL after pulmonary resection.
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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 | 0.005 | 0.021 |
| 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 | 0.000 | 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".