Compensatory function change by segment-counting method in predicted postoperative pulmonary function at 1 year after surgery: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This systematic review aimed to assess the accuracy of the segment-counting method in predicting long-term pulmonary function recovery and investigate compensatory changes following different extents of lung resection. METHODS: We included studies that measured forced expiratory volume at 1 s (FEV1) between 6 and 18 months postoperatively, comparing it to the predicted postoperative FEV1 (ppoFEV1) using the segment-counting method. The extent of lung resection was correlated with the ratio of postoperative FEV1 to ppoFEV1. A comprehensive search was conducted in Embase, MEDLINE and Web of Science using terms related to 'lung resection' and 'pulmonary function'. The final search was completed on 18 February 2022. Risk of bias was assessed using the Newcastle-Ottawa Scale. RESULTS: 39 studies comprising 78 observation cohorts met the inclusion criteria. The analysis showed significant differences in pulmonary function in patients with ≥3 resected segments. Meta-regression indicated that the number of resected segments significantly impacted the postoperative FEV1/ppoFEV1 ratio, explaining 57% of the variance (R²=0. 57), with moderate heterogeneity (I²=61. 87%) across studies. Other variables, including patient age, body mass index, video-assisted thoracoscopic surgery use and tumour stage, did not show significant effects. DISCUSSION: Limitations of the review included moderate heterogeneity between studies and potential selection bias related to the stage of cancer and lung volume reduction effects. The findings suggest that the extent of lung resection correlates with better-than-expected pulmonary function, potentially due to compensatory mechanisms. PROSPERO REGISTRATION NUMBER: This review was registered on PROSPERO (CRD42021293608).
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".