P-097PREOPERATIVE EVALUATION FOR LUNG RESECTION IN BRONCHIECTASIS PATIENTS: SHOULD WE RELY UPON STANDARD PREDICTED POSTOPERATIVE VALUES?
Bibliographic record
Abstract
Objectives: Predicted postoperative (PPO) lung function values are frequently used to define functional operability in lung cancer patients. However, due to the peculiar lung damage associated with bronchiectasis, this method could be inaccurate in such scenario. Therefore, we aimed to evaluate the accuracy of ppoFEV1, DLCO and VO2 in lung resection for bronchiectasis. Methods: Prospective study evaluating the lung function test and cardiopulmonary exercise test of individuals with symptomatic non-cystic fibrosis bronchiectasis preoperatively, 3 months and 9 months after lung resection. Predicted values were calculated as a function of observed preoperative values and the number of resected segments as proposed for the preoperative assessment of patients with lung cancer candidates for resection. Results: Forty-four patients [42.1 (±13.2) years and 50% male] completed the 9-month follow-up period. Tuberculosis was the most frequent aetiology (56.8%). 18% of the procedures were pneumonectomy, 78% lobectomy. Median ICU stay was 2 days (IQ 0-3.25) and hospital stay was 7 days (IQ 5-12.5). 11 (25%) patients developed complications, 3 patients need reoperation (1 in the first 30 days and the other 2 months after the procedure) and 2 patients died. More than 70% of the studied patients had observed values higher than that predicted. Conclusions: PPO approach underestimates the real postoperative lung function values in patients with symptomatic bronchiectasis who undergo anatomic resection. Disclosure: No significant relationships.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".