Sex-based differences in the outcomes of patients with lung carcinoids
Bibliographic record
Abstract
Objective: To assess the impact of sex on the outcomes of patients with well-differentiated lung neuroendocrine neoplasms in a real-world setting. Methods: The Surveillance, Epidemiology and End Results Research Plus database (2000–2018) was accessed, and patients with a diagnosis of typical or atypical carcinoid of the lung were reviewed. Trends in age-standardized rates (per 100,000) of the incidence of lung carcinoid tumors were reviewed among male and female patients as well as the overall population, and annual percent change (APC) was determined for the three groups. Multivariate Cox regression analysis was then used to assess the factors associated with overall and cancer-specific survival. Results: Among all patients, APC (2000–2018) for lung carcinoid diagnosis was 2.9 (95% CI: 2.4–3.5). Among male patients, APC (2000–2018) for lung carcinoid diagnosis was 1.8 (95% CI: 1.2–2.5). By contrast, among female patients, APC (2000–2018) for lung carcinoid diagnosis was 3.4 (95% CI: 2.8–4.1). Based on Kaplan–Meier survival estimates, female sex was associated with better overall survival compared with male sex (p < 0.001). Based on multivariate Cox regression analysis, the following factors were associated with worse cancer-specific survival: older age (hazard ratio [HR]: 1.036; 95% CI: 1.031–1.041), atypical carcinoid histology (HR: 3.10; 95% CI: 2.71–3.56), stage (distant vs localized stage HR: 4.05; 95% CI: 3.48–4.71), sex (male vs female sex HR: 1.76; 95% CI: 1.56–1.99) and no surgical treatment (HR: 3.77; 95% CI: 3.22–4.42). Conclusion: Female patients with lung carcinoid tumors have better overall survival compared with male patients, particularly among patients with typical carcinoid tumors.
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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 | 0.004 |
| 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 | 0.001 |
| 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".