Is Spirometry a Sufficient Test for Assessing Respiratory Function after Lung Resection?
Bibliographic record
Abstract
BACKGROUND: The prediction of postoperative functional status in non-small cell lung cancer patients based on preoperative assessment of physical and respiratory capacity is inadequate based on recent RCTs. MATERIAL AND METHODS: Prospectively collected spirometry data and the six-minute walk test results of 57 patients treated with lobectomy for non-small cell lung cancer were analyzed. The tests were performed before surgery, and 30 and 90 days after lobectomy. All patients underwent a respiratory functional and physical capacity assessment. RESULTS: All 57 patients underwent lobectomy. Before surgery, mean FEV1 was 2.4 ± 0.7 L, corresponding to %FEV1 of 88.3 ± 17.3%. The mean absolute and expected 6MWT distance was 548 ± 74.6 m and 108.9 ± 14.5%, respectively. At the first postoperative evaluation 30 days after surgery, FEV1 and %FEV1 decreased significantly by an average of 0.5 ± 0.3 L and 15.1 ± 10.7%, while 6MWT and expected 6MWT decreased minimally by an average of 1.0 m and 0.8%, respectively. Three months after lobectomy, FEV1 and %FEV1, compared with the initial assessment, decreased by an average of 0.3 ± 0.3 l and 7.8 ± 10.0%, while 6MWT and its expected score increased to 564.6 ± 84.6 m and 112.8 ± 15.8%, respectively. CONCLUSIONS: After lobectomy, FEV1 decreased slightly and less than expected, while 6MWT increased proportionally compared to the preoperative evaluation.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".