Population-Based Longitudinal Study of Follow-Up Care for Breast Cancer Survivors
Bibliographic record
Abstract
PURPOSE: To describe the patterns of follow-up care provided to a population-based cohort of breast cancer survivors, and to assess factors associated with adherence to guidelines on follow-up care. PATIENTS AND METHODS: We conducted a retrospective longitudinal study of all women with surgically treated breast cancer who were without evidence of recurrence, advanced breast cancer, or new primary cancer and were diagnosed in Ontario, Canada, within a 2-year period (n = 11,219). They were followed for 5 years. The cohort was identified through the Ontario Cancer Registry, and individuals were linked across population-based administrative health databases. Frequency of and adherence to guideline recommendations for oncologist and primary care physician (PCP) visits; surveillance imaging for metastatic disease; and surveillance mammograms by year from diagnosis, age group, and income quintile were analyzed. Factors associated with adherence to guideline recommendations were analyzed. RESULTS: Most women saw both oncologists and PCPs in each follow-up year. Approximately two thirds had surveillance mammograms in each follow-up year. Overall, two thirds had either fewer or greater than recommended oncology visits, one quarter had fewer than recommended surveillance mammograms, and half had greater than recommended surveillance imaging for metastatic disease. CONCLUSION: This population-based study shows substantial variation in adherence to guideline recommendations, with both overuse and underuse of surveillance visits and tests. Most importantly, a substantial proportion are receiving more than recommended imaging for metastatic disease but fewer than recommended mammograms for detection of local recurrence or new primary cancer, for which effective intervention is possible.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".