Prognostic Factors for Patients with Small-Cell Lung Cancer Treated with Chemoimmunotherapy: A Retrospective Multicenter Study
Bibliographic record
Abstract
Background: This study aimed to investigate prognostic factors for predicting the survival of patients with extensive-disease-stage small-cell lung cancer treated with chemoimmunotherapy. Methods: Patients were classified according to overall survival (OS): favorable corresponded to an OS ≥ 24 months, moderate corresponded to an OS of 6–24 months, and poor corresponded to an OS < 6 months. Multivariate Cox regression analyses were used to evaluate prognostic factors. Results: Of 130 patients, the proportions of performance status decline and liver metastasis were significantly higher in the poor-prognosis group. With regard to the laboratory findings, neutrophil/lymphocyte ratios and albumin levels differed significantly among the groups. Multivariate analysis showed that the independent prognostic factors for OS were liver metastasis and decreased albumin levels (<3.5 mg/dL). After classifying the patients into three groups according to the quantities of these prognostic factors, the OS differed significantly among the groups (18.3 vs. 13.5 vs. 3.8 months; p < 0.001). The incidence of immune-related adverse events (irAEs) was higher in patients without these prognostic factors than in those with both (36% vs. 5%; p = 0.01). Conclusion: Liver metastasis and decreased albumin levels are independent unfavorable prognostic factors. Patients with both prognostic factors showed unfavorable OS; however, patients without these factors may have a favorable prognosis but be at greater risk of irAEs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".