Cost Disparities with Age in the Treatment of Advanced Non-Small-Cell Lung Cancer (NSCLC) in Ontario, Canada
Bibliographic record
Abstract
Previous studies have noted associations between age and healthcare costs in non-small-cell lung cancer (NSCLC). However, the drivers of cost disparities have not yet been fully examined. This retrospective cohort study included deceased patients diagnosed with stage IV NSCLC in Ontario from 1 April 2008 to 30 March 2014. Variables of interest were extracted from the Institute for Clinical Evaluative Sciences. Average monthly cancer-attributable costs (CACs), defined as the net additional costs due to cancer, determined by subtracting pre-diagnosis costs from post-diagnosis costs, were calculated by phases of care (staging, initial, continuing, and end-of-life). Regression analyses assessed predictors of cost variability. The median age of the 14,655 patients was 65 to 69 years; 54% were male and 29% had received chemotherapy. On both univariate and multivariate analysis, CACs decreased with age after cancer diagnosis across all phases of care (p < 0.001). Receiving chemotherapy contributed to higher costs in staging, initial, and continuing phases (OR 2.11, 95% C.I. 1.90–2.33, p < 0.01), and lower costs in the end-of-life phase (OR 0.77, 95% C.I. 0.72–0.81, p < 0.01). Our study showed that older patients had higher baseline healthcare costs and lower cancer-attributable costs following diagnosis of advanced NSCLC. Cost drivers, including treatment and gender, varied by phase of care.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".