Wait Times and Survival in Lung Cancer Patients across the Province of Quebec, Canada
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death worldwide, with a five-year survival of 22% in Canada. Guidelines recommend rapid evaluation of patients with suspected lung cancer, but the impact on survival remains unclear. We reviewed medical records of all patients with newly diagnosed lung cancer in four hospital networks across the province of Quebec, Canada, between 1 February and 30 April 2017. Patients were followed for 3 years. Wait times for diagnosis and treatment were collected, and survival analysis using a Cox regression model was conducted. We included 1309 patients, of whom 39% had stage IV non-small cell lung cancer (NSCLC). Median wait times were, in general, significantly shorter in patients with stage III-IV NSCLC or SCLC. Surgery was associated with delays compared to other types of treatments. Median survival was 12.9 (11.1-15.7) months. The multivariate survival model included age, female sex, performance status, histology and stage, treatment, and the time interval between diagnosis and treatment. Longer wait times had a slightly protective to neutral effect on survival, but this was not significant in the stage I-II NSCLC subgroup. Wait times for the diagnosis and treatment of lung cancer were generally within targets. The shorter wait times observed for advanced NSCLC and SCLC might indicate a tendency for clinicians to act quicker on sicker patients. This study did not demonstrate the detrimental effect of longer wait times on survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".