Bibliographic record
Abstract
Background: Despite universal access to health care in Canada, disparities relating to social determinants of health contribute to discrepancies in cancer incidence and outcomes between rural and urban areas. Given that Canada has one of the highest-quality national population-based cancer registry systems in the world and little information is available about cancer statistics specific to northwestern Ontario, the purpose of this study was to estimate the percentage of cancer patients without documentation of a specialist consultation (medical or radiation oncology consultation) and to determine factors that affect access to specialist consultation in northwestern Ontario. Methods: This population-based retrospective study used administrative data obtained through the Ontario Cancer Data Linkage Project. For each index case, a timeline was constructed of all Ontario Health Insurance Plan billing codes and associated service dates, starting with the primary cancer diagnosis and ending with death. Specific factors affecting access to specialist consultation were assessed. Results: Within the 6-year study period (2010–2016), 2583 index cases were identified. Most (n=2007, 78%) received a specialist consultation. Factors associated with not receiving a specialist consultation included older age [p<0.0001; odds ratio (or): 0.29; 95% confidence interval (ci): 0.19 to 0.44] and rural residence (p<0.0001; or: 0.48; 95% ci: 0.48 to 0.72). Factors associated with receiving a specialist consultation included increased duration of disease (p<0.0001; or: 1.32; 95% ci: 1.19 to 1.46), a diagnosis of breast cancer (p<0.0001; or: 2.51; 95% ci: 1.43 to 4.42), and a diagnosis of lung cancer (p<0.0001; or: 1.77; 95% ci: 1.38 to 2.26). Conclusions: This study is the first to look at care access in northwestern Ontario. The complexity and multidisciplinary nature of cancer care make the provision of appropriate care a challenge; a one-size-fits-all disease prevention and treatment strategy might not be appropriate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.577 | 0.406 |
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".