Interstitial lung disease is associated with an increased risk of lung cancer in systemic sclerosis: Longitudinal data from the Canadian Scleroderma Research Group
Bibliographic record
Abstract
Objective: The literature supports an increased risk of malignancy in systemic sclerosis, including lung cancer. Our objective was to identify potential independent predictors of lung cancer risk in systemic sclerosis. Methods: We used a cohort of 1560 systemic sclerosis patients from the Canadian Scleroderma Research Group, enrolled from 2004 and followed for a maximum of 11 years. Time to lung cancer was calculated from the onset of the first non-Raynaud's symptoms. Baseline demographic, clinical, and serological characteristics of patients with and without lung cancer were compared. Cox proportional hazards models were used to estimate the effects of demographic variables, exposure to smoking, disease duration, disease subset (diffuse vs limited), immunosuppressant drug exposure, and presence of interstitial lung disease on the risk of lung cancer. Results: Over the 5519 total person-years of follow-up, 18 SSc patients were diagnosed with lung cancer after cohort entry (3.2 cancers per 1000 person-years). In univariate comparisons, cancer cases were more likely to be male, to have a smoking history, and to have interstitial lung disease than non-cases. In multivariate analysis, interstitial lung disease was independently associated with the risk of lung cancer (hazard ratio: 2.95, 95% confidence interval: 1.10-7.87). Conclusion: In addition to known demographic (male sex) and lifestyle risk factors (smoking), interstitial lung disease is an independent risk factor for lung cancer in systemic sclerosis. These results have implications for lung cancer screening in systemic sclerosis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".