Peak oxygen consumption and long‐term all‐cause mortality in nonsmall cell lung cancer
Bibliographic record
Abstract
BACKGROUND: Identifying strong markers of prognosis is critical to optimize treatment and survival outcomes in patients with nonsmall cell lung cancer (NSCLC). The authors investigated the prognostic significance of preoperative cardiorespiratory fitness (peak oxygen consumption [VO(2peak)]) among operable candidates with NSCLC. METHODS: By using a prospective design, 398 patients with potentially resectable NSCLC enrolled in Cancer and Leukemia Group B 9238 were recruited between 1993 and 1998. Participants performed a cardiopulmonary exercise test to assess VO(2peak) and were observed until death or June 2008. Cox proportional models were used to estimate the risk of all-cause mortality according to cardiorespiratory fitness category defined by VO(2peak) tertiles (<0.96 of 0.96-1.29/>1.29 L/min⁻¹) with adjustment for age, sex, and performance status. RESULTS: Median follow-up was 30.8 months; 294 deaths were reported during this period. Compared with patients achieving a VO(2peak) <0.96 L/min⁻¹, the adjusted hazard ratio (HR) for all-cause mortality was 0.64 (95% confidence interval [CI], 0.46-0.88) for a VO(2peak) of 0.96 to 1.29 L/min⁻¹, and 0.56 (95% CI, 0.39-0.80) for a VO(2peak) of >1.29 L/min⁻¹) (P(trend) = .0037). The corresponding HRs for resected patients were 0.66 (95% CI, 0.46-0.95) and 0.59 (95% CI, 0.40-0.89) relative to the lowest VO(2peak) category (P(trend) = .0247), respectively. For nonresected patients, the HRs were 0.78 (95% CI, 0.34-1.79) and 0.39 (95% CI, 0.16-0.94) relative to the lowest category (P(trend) = .0278). CONCLUSIONS: VO(2peak) is a strong independent predictor of survival in NSCLC that may complement traditional markers of prognosis to improve risk stratification and prognostication.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".