Ambient air pollution and survival in SCLC/LCNEC: Analysis of a single centre retrospective cohort
Bibliographic record
Abstract
Background Small cell lung cancer (SCLC) and large cell neuroendocrine carcinoma of the lung (LCNEC) are the deadliest forms of lung cancer with dismal prognosis. Recent evidence suggests that, beyond cigarette smoke, air pollution can have a role in the pathogenesis of non-small cell lung cancer (NSCLC) and is associated with poorer survival. However, whether air pollutants exposure could affect survival outcomes in SCLC/LCNEC is unknown. Methods We retrospectively analysed data from SCLC/LCNEC cases observed in the province of Brescia province Brescia between 2017 and 2021. Air pollutants mean concentrations were calculated during the same timeframe and the Brescia province was divided in six subareas dichotomized into lightly and heavily polluted areas based on the mean PM 2.5 concentrations. Primary endpoint was to determine the impact of air pollutants exposure on SCLC/LCNEC overall survival (OS). Additionally, we explored the distribution of SCLC/LCNEC across the subareas classified for different air pollutants concentrations. Findings We observed 221 cases of SCLC/LCNEC, accounting for about 18% of new lung cancer cases. Residency in heavily polluted areas (HR 1.51, p=0.03) and extensive stage disease at diagnosis (HR 2.47, p=0.0001) emerged as independent factors for poorer survival. Exploratory analyses showed an association between the distribution of SCLC/LCNEC cases and higher PM 10 and NO 2 concentrations (OR 1.16, p<0.001 and OR 1.46, p<0.001, respectively). Interpretation These results indicate that long-term exposure to high levels of PM 2.5 represent an independent unfavourable prognostic factor for SCLC/LCNEC. Our data suggest that air pollution may also favour the onset of these malignant diseases. Case-control studies are warranted to confirm these preliminary results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".