The association between cirrhosis and outcomes among patients with lung cancer in Ontario between 2007 and 2017: a population-based study
Bibliographic record
Abstract
Abstract Introduction: We describe the association between cirrhosis and outcomes among patients treated for non-small cell lung cancer (NSCLC). Methods: NSCLC cases in 2007–2017 in Ontario, Canada, were reviewed. Cirrhosis was identified using validated coding. The association between cirrhosis and outcomes was evaluated using logistic regression, Kaplan–Meier curves, and adjusted survival models. Results: Among patients with stage 1–3 NSCLC receiving lung resection (n = 59,226), 3% had cirrhosis with higher 30-day (5% versus 2%, p < 0.001) and 90-day (8% versus 3%, p < 0.001) mortality. After multivariable logistic regression, cirrhosis was associated with higher mortality odds at 30 days (OR 2.35, 95% CI 1.39–3.99, p < 0.001) and 90 days (OR 2.10, 95% CI 1.38–3.21, p < 0.001); higher 90-day post-operative complications odds (OR 1.44, 95% CI 1.14–1.81, p = 0.002); and higher hospital readmission odds at 30 days (OR 1.90, 95% CI 1.41–2.56, p < 0.001) and 90 days (OR 1.63, 95% CI 1.27–2.10, p = 0.001). Among those with stage 4 NSCLC (n = 27,357), adjusted Cox regression showed cirrhosis was associated with higher mortality (HR 1.10, 95% CI 1.02–1.18, p = 0.016). However, when liver-related mortality was a competing event, association between cirrhosis and cancer-specific mortality was absent (sHR 1.04, 95% CI 0.95–1.13, p = 0.395). A lower proportion of patients with cirrhosis received systemic treatments (31% versus 40%, p < 0.001), and a greater proportion experienced post-treatment complications: electrolyte disturbances (10% versus 5%, p < 0.001), kidney injury (5% versus 2%, p = 0.012), and thrombocytopenia (7% versus 2%, p < 0.001). Conclusions: Cirrhosis is associated with increased morbidity and mortality in stage 1–3 NSCLC patients undergoing curative surgery. Stage 4 NSCLC patients with cirrhosis are less likely to receive palliative treatment and may experience more post-treatment complications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".