Abstract A10: Using large linked databases to understand cancer care access in northwestern Ontario, Canada
Bibliographic record
Abstract
Abstract Residents in Ontario, Canada have universal public health insurance, and the Ontario Health Insurance Plan (OHIP) and almost all medically necessary services are captured in large linked databases. We leveraged these databases through the Ontario Cancer Data Linkage Project (cdLINK), to help us better understand access to cancer care for patients who reside in Northwestern Ontario, Canada. Northwestern Ontario is an area that covers a large geographic landmass (>385,000 square kilometers) with low population density (0.4 people per square kilometer), and these factors often make the provision of health care challenging. This population-based retrospective study used the Ontario Cancer Registry (OCR) to define a cancer cohort of all residents of Northwestern Ontario who had a primary diagnosis of cancer, who were alive for at least 30 days, and who died from any cause between 2010 and 2016. Each index cancer case was linked with OHIP and Registered Persons Database (RPDB), and from these administrative sources we defined whether a consultation occurred, as well as selected sociodemographic factors associated with a consultation. We identified 2583 index cases (2010-2016). Of these, 78% (n=2007/2583) had receipt of a specialist consultation, defined as either a medical or radiation oncologist consultation. Factors associated with not receiving a specialist consultation included older age (p<0.0001, Odds Ratio (OR) 0.29; 95% Confidence Interval (CI) 0.19-0.44) and rural residence (p<0.0001, OR 0.48; 95% CI 0.48-0.72). Factors associated with receiving a specialist consultation included increased duration of disease (p<0.0001, OR 1.32; 95% CI 1.19-1.46), a diagnosis of breast cancer (p<0.0001, OR 2.51; 95% CI 1.43-4.42), and a diagnosis of lung cancer (p<0.0001, OR 1.77; 95% CI 1.38-2.26). Linkage of administrative health records offers an important opportunity to understand access to care and factors associated with access to care. Future planned research will explore health services utilization and outcomes from cancer care in residents of Northwestern Ontario. Citation Format: Michela Febbraro, Michael Conlon, Joseph Caswell, Nicole Laferierre. Using large linked databases to understand cancer care access in northwestern Ontario, Canada [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A10.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".