Ventilation defect burden predicts lung cancer resection outcomes
Bibliographic record
Abstract
Background Abnormal ventilation prior to lung cancer resection has not been investigated using modern ventilation imaging modalities and may better predict postoperative outcomes than guideline-recommended lung function tests. Our objective was to quantify the burden of ventilation defects observed using Technegas single-photon emission computed tomography (SPECT) and 129Xe magnetic resonance imaging (MRI) before lung cancer resection, and to evaluate their association with postoperative pulmonary complications and length of hospital stay. Methods This was a prospective, 6-week, observational study of adults undergoing lung cancer resection at a single centre. Before lung resection, participants underwent Technegas-SPECT, 129Xe-MRI, spirometry and measurement of diffusing capacity of the lung for carbon monoxide. Preoperative ventilation defect burden was quantified by the Technegas-SPECT and 129Xe-MRI ventilation defect percent (VDP). Predictors of complications during the 4-week postoperative period and length of hospital stay were evaluated by logistic and linear regression. Results Abnormal ventilation was observed preoperatively by Technegas-SPECT and 129Xe-MRI for 58% (60 of 103) and 73% (74 of 102) of participants, respectively. Preoperative VDPs were higher for participants with postoperative complications compared with those without (SPECT: p=0.01; MRI: p=0.0006) and correlated with length of hospital stay (SPECT: r=0.44, p<0.0001; MRI: r=0.51, p<0.0001). Multivariable models revealed preoperative VDP to be the strongest predictor of postoperative complications (SPECT: OR 1.06, 95% CI 1.01–1.11, p=0.02; MRI: OR 1.11, 95% CI 1.02–1.21, p=0.02) and length of hospital stay (SPECT: β=0.16, p<0.001; MRI: β=0.23, p<0.001). Conclusion Abnormal ventilation is prevalent prior to lung cancer resection and may be a stronger predictor of postoperative complications and length of hospital stay than standard clinical lung function measures.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".