Survivorship care in breast cancer
Bibliographic record
Abstract
Objective To compare the perceptions of breast cancer survivors and primary care physicians (PCPs) about PCPs’ ability to deliver survivorship care in breast cancer. Design Mailed survey. Setting British Columbia. Participants A total of 1065 breast cancer survivors who had completed treatment of nonmetastatic breast cancer within the previous year, and 587 PCPs who had patients with nonmetastatic breast cancer discharged to their care within the preceding 18 months. Main outcome measures Breast cancer survivors’ and PCPs’ confidence ratings of PCPs’ ability to deliver the following aspects of care: screening for recurrence; managing osteoporosis, lymphedema, endocrine therapy, menopausal symptoms, and anxiety about or fear of recurrence; and providing nutrition and exercise counseling, sex and body image counseling, and family counseling. Response options for each question included low, adequate, or good. Responses were summarized as frequencies and compared using χ2 tests. Results Response rates for breast cancer survivors and PCPs were 47% and 59%, respectively. Responses were statistically different in all categories ( P < .05). Both groups were most confident in the ability of PCPs to screen for recurrence, but breast cancer survivors were 10 times as likely to indicate low confidence (10% of breast cancer survivors vs 1% of PCPs) in this aspect of care. More breast cancer survivors (23%) expressed low confidence in PCPs’ ability to provide counseling about fear of recurrence compared with PCPs (3%). Aspects of care in which both breast cancer survivors and PCPs were most likely to express low confidence included sex and body image counseling (35% of breast cancer survivors vs 26% of PCPs) and family counseling (33% of breast cancer survivors vs 24% of PCPs). Primary care physicians (24%) described low confidence in their ability to manage lymphedema. Conclusion Breast cancer survivors and PCPs are reasonably confident in a PCP-based model of survivorship care. Primary care physicians are confident in their ability to manage physical effects related to breast cancer, with the exception of lymphedema. Low confidence ratings among both groups in psychosocial aspects of care suggest an area for improvement.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".