Physicians’ Beliefs About Breast Cancer Surveillance Testing are Consistent With Test Overuse
Bibliographic record
Abstract
BACKGROUND: Overuse of surveillance testing for breast cancer survivors is an important problem but its extent and determinants are incompletely understood. The objectives of this study were to determine the extent to which physicians' breast cancer surveillance testing beliefs are consistent with test overuse, and to identify factors associated with these beliefs. METHODS: During 2009-2010, a cross-sectional survey of US medical oncologists and primary care physicians (PCPs) was carried out. Physicians responded to a clinical vignette ascertaining beliefs about appropriate breast cancer surveillance testing. Multivariable analyses examined the extent to which test beliefs were consistent with overuse and associated with physician and practice characteristics and physician perceptions, attitudes, and practices. RESULTS: A total of 1098 medical oncologists and 980 PCPs completed the survey (response rate 57.5%). Eighty-four percent of PCPs [95% confidence interval (CI), 81.4%-86.5%] and 72% of oncologists (95% CI, 69.8%-74.7%) reported beliefs consistent with blood test overuse, whereas 50% of PCPs (95% CI, 47.3%-53.8%) and 27% of oncologists (95% CI, 23.9%-29.3%) reported beliefs consistent with imaging test overuse. Among PCPs, factors associated with these beliefs included smaller practice size, lower patient volume, and practice ownership. Among oncologists, factors included older age, international medical graduate status, lower self-efficacy (confidence in knowledge), and greater perceptions of ambiguity (conflicting expert recommendations) regarding survivorship care. CONCLUSIONS: Beliefs consistent with breast cancer surveillance test overuse are common, greater for PCPs and blood tests than for oncologists and imaging tests, and associated with practice characteristics and perceived self-efficacy and ambiguity about testing. These results suggest modifiable targets for efforts to reduce surveillance test overuse.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".