A comparison of primary care providers’ and oncologists’ preferences for different models of cancer survivorship care.
Bibliographic record
Abstract
6006 Background: There is increasing interest in developing more efficient and effective strategies for coordinating and delivering cancer and non-cancer related follow-up care to survivors. The objectives of this nationwide survey were to describe and compare US physician preferences for different cancer survivorship care models. Methods: The Survey of Physician Attitudes Regarding the Care of Cancer Survivors (SPARCCS) was mailed to PCPs and oncologists in order to evaluate their views regarding physician responsibilities, knowledge levels about survivorship, and cancer follow-up testing. Using weighted univariate and multivariate models, we analyzed PCPs’ and oncologists’ preferences for different cancer survivorship care models (PCP/shared vs. oncologist vs. non-physician provider) and examined how physician attitudes towards and self-efficacy with their own skills during breast and colorectal cancer follow-up affected these preferences. Results: Of 3,434 physicians surveyed, 2,202 (64%) responded of whom 2,026 (59%) provided eligible outcomes for this study: 938 (46%) PCPs and 1,088 (54%) oncologists. In unadjusted analyses, most PCPs (51%) supported a PCP/shared care system whereas the majority of specialists (59%) strongly endorsed an oncologist-based model (p<0.001). A number of PCPs and oncologists (23% for both) preferred to involve non-physician providers. A significant proportion of cancer specialists (87%) did not feel that PCPs can take on the primary role for cancer follow-up. Many PCPs believed that they have the skills to perform breast and colorectal cancer follow-up (57%), detect recurrent cancers (74%), and offer psychosocial support (50%), but only a minority (32%) were willing to assume exclusive responsibility. In adjusted analyses, PCPs already involved with cancer surveillance (43%) were more likely to prefer a PCP/shared care system than an oncologist-based survivorship care model (OR 2.08, 95%CI 1.34-3.23, p<0.001). Conclusions: PCPs and oncologists have different preferences for models of cancer survivorship care. Prior involvement with cancer follow-up was one of the strongest predictors of PCPs' willingness to assume this responsibility.
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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 | 0.003 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".