Ascertaining cancer survivors in Ontario using the Ontario Cancer Registry and administrative data.
Bibliographic record
Abstract
34 Background: The number of cancer survivors in Ontario has grown rapidly due to increasing incidence and advances in screening, diagnostic technologies and treatment. However, there is a lack of information to plan, monitor and improve follow-up care. Using the Ontario Cancer Registry (OCR) and health services administrative data, we developed a cohort of cancer survivors from which we could determine demographic characteristics, where follow-up care was received, and concordance with guideline-recommended surveillance testing. Methods: Individuals were included in the cumulative survivor cohort if they had at least one diagnosed incident malignant cancer from 1964 to 2017 in the OCR. Patients were considered survivors upon completion of treatment (surgery, chemotherapy, radiation therapy). Treatment was ascertained from clinical and administrative data using a data-driven approach combined with clinical expert input. In the absence of recurrence data, a treatment-based proxy was developed. Stage IV and complex malignant haematology cancer patients were excluded. We did a cross-sectional analysis of survivors in the cohort in 2016. We produced descriptive statistics and also determined the year of survivorship. For those who were in their first to fifth year of survival, we calculated the proportion who saw a medical or radiation oncologist (MO/RO) in 2016 stratified by year of survivorship. Results: As of December 31, 2016, there were 414,134 cancer survivors in the cohort, roughly 3% of the Ontario population. Ninety-three percent of survivors had a single primary cancer diagnosis, 66% were aged 65 or older, and slightly more were female (55%). Also, 22% had been diagnosed with breast cancer, 22% with prostate, and 12% with colorectal cancer. For those in their first year of survivorship, roughly 50% saw a MO/RO; whereas, for those in their fifth year of survival, 36% had seen a MO and 27% had seen an RO. Conclusions: The development of a cancer survivor cohort has enabled us to produce timely data on a previously unidentified patient population. Linking this cohort with existing administrative data will enable further examination of visit trajectories as well as cancer and non-cancer health outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".