Socioeconomic disparities in head and neck cancer patients’ access to cancer treatment centers
Bibliographic record
Abstract
INTRODUCTION: Both socioeconomic status and travel time to cancer treatment have been associated with treatment choice and patient outcomes. An improved understanding of the relationship between these two dimensions of access may enable cancer control experts to better target patients with poor access, particularly in isolated suburban and rural communities. METHODS: Using geographical information systems, head and neck cancer patients across British Columbia, Canada from 1981 to 2009, were mapped and their travel times to the nearest treatment center at their time of diagnosis were modelled. Patients' travel times were analysed by urban, suburban, and rural neighborhood types and an index of multiple socioeconomic deprivation was used to assess the role of socioeconomic status in patients' spatial access. RESULTS: Significant associations between socioeconomic deprivation and spatial access to treatment were identified, with the most deprived quintiles of patients experiencing nearly twice the travel time as the least deprived quintile. The sharpest disparities were observed among the most deprived patient populations in suburban and rural areas. However, the establishment of new treatment centers has decreased overall travel times by 28% in recent decades. CONCLUSIONS: Residence in a neighborhood with high socioeconomic deprivation is strongly associated with head and neck cancer patients' spatial access to cancer treatment centers. Patients residing in the most socioeconomically deprived neighborhoods consistently have longer travel times in urban, suburban, and rural communities in the study area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".