Associations Among Cancer Survivorship Discussions, Patient and Physician Expectations, and Receipt of Follow-Up Care
Bibliographic record
Abstract
PURPOSE: To explore the associations among cancer survivorship discussions, patient-physician expectations, and receipt of follow-up care in cancer survivors. PATIENTS AND METHODS: We surveyed cancer survivors about various aspects of their care, including expectations of their providers' roles, whether discussions with a physician had occurred, and self-reported patterns of follow-up. Primary care providers (PCPs) and oncologists were also surveyed for their own perceived roles. We developed a scoring system to evaluate the level of agreement in expectations between patients and physicians and between PCPs and oncologists (where 0 = most discordant and 4 = most concordant). Regression and stratified analyses were conducted to examine the relationships among expectations, discussions, and follow-up. RESULTS: In total, 535 patients (54%) and 378 physicians (62%) responded. Survivorship care expectations were most discrepant between PCPs and oncologists (mean score, 1.78), moderate between patients and oncologists (mean score, 1.97), and most similar between patients and PCPs (mean score, 2.82). Having a conversation specifically about cancer follow-up was associated with better concordance between patients and oncologists, but not for patients and their PCPs or between physicians. Better concordance in patient-oncologist expectations also correlated with greater odds of receiving certain aspects of follow-up care, such as influenza vaccinations and physical examinations, but only if a discussion about cancer follow-up had occurred. CONCLUSION: A discussion about cancer follow-up may affect survivorship care through its primary influence on patient-oncologist expectations. Further work is required to clarify the aspects of survivorship discussions that are important for optimal cancer survivorship care planning.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".