A lower level of forced expiratory volume in one second predicts the poor prognosis of small cell lung cancer
Bibliographic record
Abstract
Background: The impact of impaired pulmonary function on the clinical outcome of small cell lung cancer (SCLC) has not been examined. The objectives of this study were to compare the clinical characteristics and prognosis of SCLC patients with and without impaired pulmonary function and investigate predictors related to the pulmonary function of mortality in SCLC patients. Methods: This is a retrospective multicenter study performed between January 2011 and December 2015. In all, 170 SCLC patients that were treated with chemotherapy and/or radiotherapy and had a pulmonary function test (PFT) were enrolled. Patients were divided into the chronic obstructive pulmonary disease (COPD) group and the non-COPD group. The overall survival (OS) was compared and predictors of worse OS were analyzed. Results: COPD was present in 54.7% of all SCLC patients. There were no differences in the clinical characteristics and treatment strategies between the COPD and non-COPD groups. OS (log-rank test, P=0.103) was not different between the COPD and non-COPD groups. In a multivariate analysis using a Cox regression model, extensive disease (ED) [hazard ratio (HR) =2.863; 95% CI: 1.787–4.587] and low forced expiratory volume in 1 second (FEV 1 ) <80% (HR =1.854; 95% CI: 1.077–3.192) were independent risk factors for shorter survival. In a subgroup multivariate analysis, a FEV 1 less than 80% (HR =5.631; P=0.018) was independently associated with poor OS in patients with ED. Conclusions: A low FEV 1 , not COPD, was a predicting factor for poor treatment outcomes in SCLC patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".