Are primary care providers implementing evidence-based care for breast cancer survivors?
Bibliographic record
Abstract
OBJECTIVE: To describe the implementation of key best practice guideline recommendations for posttreatment breast cancer survivorship care by primary care providers (PCPs). DESIGN: Descriptive cross-sectional survey. SETTING: Southeastern Ontario. PARTICIPANTS: Eighty-two PCPs: 62 family physicians (FPs) and 20 primary health care nurse practitioners (PHCNPs). MAIN OUTCOME MEASURES: Twenty-one “need-to-know” breast cancer survivorship care guideline recommendations rated by participants as “implemented routinely,” “aware of guideline recommendation but not implemented routinely,” or “not aware of guideline recommendation.” RESULTS: Overall, FPs and PHCNPs in our sample reported similar practice patterns in terms of implementation of breast cancer survivorship guideline recommendations. The PCPs reported routinely implementing approximately half (46.4%, 9.7 of 21) of the key guideline recommendations with breast cancer survivors in their practices. Implementation rates were higher for recommendations related to prevention and surveillance aspects of survivorship care, such as mammography and weight management. Knowledge and practice gaps were highest for recommendations related to screening for and management of long-term effects such as fatigue and distress. There were only a few minor differences reported between FPs and PHCNPs. CONCLUSION: There are knowledge and practice gaps related to implementation of the key guideline recommendations for breast cancer survivorship care in the primary care setting that could be targeted for improvement through educational or other interventions.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".