Using Canadian administrative data to evaluate primary and oncology care of breast cancer patients post-treatment: Subset of the CanIMPACT Study.
Bibliographic record
Abstract
5 Background: CanIMPACT is a multi-provincial Canadian research team funded to identify and address key issues faced by cancer patients and providers at the intersection of primary and specialist oncology care. Canada has national healthcare standards, but provincial/territorial healthcare delivery systems. One facet will use administrative data from the population-based, publicly-funded healthcare system to evaluate issues during pre-diagnosis, treatment, and post-treatment survivorship for breast cancer patients. For the survivorship phase, we aim to conduct the following analyses and compare across provinces: 1) Utilization of physician services overall and by specialty, including oncologists, non-oncology specialists, and primary care; 2) Assessment of adherence to ASCO and Canadian follow-up guideline for breast cancer care, use of surveillance breast imaging, and metastatic investigations; 3) Assessment of adherence to recommended care of chronic illness and preventive care; 4) Quantification of the cost of follow-up overall and by specialty; 5) Comparison of inter- and intra-provincial variation for all outcomes by health administrative region and for vulnerable groups (age ≥ 75 at diagnosis, northern/rural/remote, low income, immigrants), and examine the effect of continuity of primary care and chronic disease on post-treatment care. Methods: Patients will be identified from provincial cancer registries and linked to data extracted from: outpatient physician service claims, hospital inpatient and outpatient data, and cancer facility medical records. Results: Participating provinces have finalized the core questions and detailed protocols, and assessed data comparability. They are in the process of obtaining the required ethics and data access approvals, and data acquisition for processing and analysis. Conclusions: Results will address existing information gaps that can be used to improve transition and care across the cancer care trajectory. Importantly, results will be combined with those of a CanIMPACT qualitative study to inform design of a pragmatic randomized trial focused on improving coordination and quality of care.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.018 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".