Access to care and outcomes for noncurative esophagogastric cancer: A population-based geographic study.
Bibliographic record
Abstract
284 Background: Esophagogastric cancer (EGC) carries a heavy mortality burden owing largely to high rates of unresectable disease at diagnosis. Among patients not undergoing curative-intent therapy, access to care may vary. We examined the geographic distribution of care delivery and survival across a jurisdiction, and its relationship with distance to cancer centres (CCs), for non-curative EGC. Methods: We conducted a population-based analysis of adults with non-curative EGC from 2005-2017 using linked administrative healthcare datasets in Ontario, Canada. Outcomes were medical oncology consultation, receipt of chemotherapy, and overall survival (OS). We used geographic information system analysis to map locations of CCs and outcomes across census divisions. Regions of discordance between care use and OS were identified with bivariate choropleth maps. Multivariable modified Poisson models assessed the relationship between distance to the nearest CC and outcomes, adjusting for demographic, clinical, and socioeconomic factors. Results: Of 10,228 patients surviving a median of 5.1 months (IQR: 2.0-12.0), 68.6% had medical oncology consultation and 32.2% received chemotherapy. Regions of comparable OS and care delivery were clustered throughout the province. CCs were distributed unevenly, with higher levels in Southern Ontario. Higher-level CCs clustered in regions with higher rates of consultation, chemotherapy use, and OS. Each increment in distance from location of residence to the nearest CC (11-50, 51-100, and ≥101 km) was associated with lower likelihood of seeing medical oncology and receiving chemotherapy, and inferior OS, compared to ≤10 km. Conclusions: A third of patients with non-curative EGC did not see medical oncology, and the majority did not receive chemotherapy. Care delivery and OS exhibited high geographic variability. Location of residence influenced access to care and OS, with inferior outcomes for those living further from a CC. These findings are important for designing interventions and policies to reduce disparities in access to care and outcomes for non-curative EGC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".