Primary care physician use across the breast cancer care continuum: CanIMPACT study using Canadian administrative data.
Bibliographic record
Abstract
OBJECTIVE: To describe primary care physician (PCP) use and continuity of PCP care across the breast cancer care continuum. DESIGN: Population-based, retrospective cohort study using provincial cancer registries linked to health administrative databases. SETTING: British Columbia, Manitoba, and Ontario. PARTICIPANTS: All women with incident invasive breast cancer from 2007 to 2012 in Manitoba and Ontario and from 2007 to 2011 in British Columbia. MAIN OUTCOME MEASURES: The number and proportions of visits to PCPs were determined. Continuity of care was measured using the Usual Provider of Care index calculated as the proportion of visits to the most-often-visited PCP in the 6 to 30 months before a breast cancer diagnosis (baseline) and from 1 to 3 years following a breast cancer diagnosis (survivorship). RESULTS: More than three-quarters of patients visited their PCPs 2 or more times during the breast cancer diagnostic period, and more than 80% of patients had at least 1 PCP visit during breast cancer adjuvant treatment. Contact with the PCP decreased over time during breast cancer survivorship. Of the 3 phases, women appeared to be most likely to not have PCP contact during adjuvant treatment, with 10.7% (Ontario) to 18.7% (British Columbia) of women having no PCP visits during this phase. However, a sizable minority of women had at least monthly visits during the treatment phase, particularly in Manitoba and Ontario, where approximately a quarter of women saw a PCP at least monthly. We observed higher continuity of care with PCPs in survivorship (compared with baseline) in all provinces. CONCLUSION: Primary care physicians were generally involved throughout the breast cancer care continuum, but the level of involvement varied across care phases and by province. Future interventions will aim to further integrate primary and oncology 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".