Physician roles in the cancer-related follow-up care of cancer survivors.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Information about primary care physicians' (PCPs) and oncologists' involvement in cancer-related follow-up care, and care coordination practices, is lacking but essential to improving cancer survivors' care. This study assesses PCPs' and oncologists' self-reported roles in providing cancer-related follow-up care for survivors who are within 5 years of completing cancer treatment. METHODS: In 2009, the National Cancer Institute and the American Cancer Society conducted a nationally representative survey of PCPs (n=1,014) and medical oncologists (n=1,125) (response rate=57.6%, cooperation rate=65.1%). Mailed questionnaires obtained information on physicians' roles in providing cancer-related follow-up care to early-stage breast and colon cancer survivors, personal and practice characteristics, beliefs about and preferences for follow-up care, and care coordination practices. RESULTS: More than 50% of PCPs reported providing cancer-related follow-up care for survivors, mainly by co-managing with an oncologist. In contrast, more than 70% of oncologists reported fulfilling these roles by providing the care themselves. In adjusted analyses, PCP co-management was associated with specialty, training in late or long-term effects of cancer, higher cancer patient volume, favorable attitudes about PCP care involvement, preference for a shared model of survivorship care, and receipt of treatment summaries from oncologists. Among oncologists, only preference for a shared care model was associated with co-management with PCPs. CONCLUSIONS: PCPs and oncologists differ in their involvement in cancer-related follow-up care of survivors, with co-management more often reported by PCPs than by oncologists. Given anticipated national shortages of PCPs and oncologists, study results suggest that improved communication and coordination between these providers is needed to ensure optimal delivery of follow-up care to cancer survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".