The supply of physicians and care for breast cancer in Ontario and California, 1998 to 2006.
Bibliographic record
Abstract
INTRODUCTION: We examined the differential effects of the supply of physicians on care for breast cancer in Ontario and California. We then used criteria for optimum care for breast cancer to estimate the regional needs for the supply of physicians. METHODS: Ontario and California registries provided 951 and 984 instances of breast cancer diagnosed between 1998 and 2000 and followed until 2006. These cohorts were joined with the supply of county-level primary care physicians (PCPs) and specialists in cancer care and compared on care for breast cancer. RESULTS: Significant protective PCP thresholds (7.75 to = 8.25 PCPs per 10 000 inhabitants) were observed for breast cancer diagnosis (odds ratio [OR] 1.62), receipt of adjuvant radiotherapy (OR 1.64) and 5-year survival (OR 1.87) in Ontario, but not in California. The number of physicians seemed adequate to optimize care for breast cancer across diverse places in California and in most Ontario locations. However, there was an estimated need for 550 more PCPs and 200 more obstetrician-gynecologists in Ontario's rural and small urban areas. We estimated gross physician surpluses for Ontario's 2 largest cities. CONCLUSION: Policies are needed to functionally redistribute primary care and specialist physicians. Merely increasing the supply of physicians is unlikely to positively affect the health of Ontarians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".