Rural vs urban inequalities in stage at diagnosis for lung cancer
Bibliographic record
Abstract
OBJECTIVES: Early diagnosis of lung cancer increases the chance of survival. The aim of this study was to measure the relationship between geographic residence in Saskatchewan and stage of lung cancer at the time of diagnosis. MATERIALS AND METHODS: Retrospective cohort analysis of 2,972 patients with a primary diagnosis of either non-small cell cancer (NSCLC) or small cell lung cancer (SCLC) between 2007 and 2012 was performed. Incidence proportion of early and advanced stage cancer, and relative risk of being diagnosed with advanced-stage lung cancer relative to early-stage was calculated. RESULTS: Compared to urban Saskatchewan, rural Saskatchewan lung cancer patients had a higher relative risk of advanced stage NSCLC (relative risk [RR] = 1.11, 95% confidence interval [CI]: 1.01-1.22). Rural Saskatchewan was further subdivided into north and south. The relative risk of advanced stage NSCLC in rural north Saskatchewan compared to urban Saskatchewan was even greater (RR = 1.17, 95% CI: 1.03-1.31). Although not statistically significant, there was a trend for a higher incidence of advanced stage SCLC in rural and rural north vs urban Saskatchewan (RR = 1.16, 95% CI: 0.95-1.43 and RR = 1.22; 95% CI: 0.94-1.58, respectively). There was a higher incidence proportion of advanced stage NSCLC in rural areas relative to urban (31.6-34.4 vs 29.5 per 10,000 people). CONCLUSION: Patients living in rural Saskatchewan have higher incidence proportion of and were more likely to present with advanced stage NSCLC in comparison to urban Saskatchewan patients at time of diagnosis. This inequality was even greater in rural north Saskatchewan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".