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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".