Follow-up of long-term young breast cancer survivors.
Bibliographic record
Abstract
8 Background: Evidence suggests breast cancer patients can be offered follow-up by family physician without concern of important recurrence–related serious clinical events occurring more frequently or quality of life being negatively affected. This study describes population-based patterns of follow-up care in 5-year recurrence-free young breast cancer survivors to determine factors influencing continued oncology follow-up in Ontario, Canada. Methods: We conducted a retrospective population-based cohort study using cancer registry and administrative data. Women diagnosed with an incident breast cancer aged 20-44 between 1992 and 1999, survived for at least 5 years and recurrence-free for 5 years past diagnosis were identified in the Ontario Cancer Registry. Each survivor was matched to 5 control women with the same calendar year of birth and place of residence in Ontario. We determined outpatient physician visits with primary care, medical, radiation and surgical oncology physicians to investigate trends associated with increasing survivorship and compared visit rates to controls. We used negative binomial regression to investigate factors predicting high utilization of oncology services among survivors after 5-year recurrence survival. Results: We identified 4,581 survivors and 22,898 controls. By year 10, 51% breast cancer survivors were still being followed by an oncologist. In the survivors, fewer physician visits were observed among recurrence-free breast cancer survivors as time increased from diagnosis (Visit Rate Ratio [VRR] =0.95, 95% CI: 0.94, 0.96). Breast cancer survivors diagnosed from 1992-1995 had a higher rate of physician visits than those diagnosed from 1996-1999 (VRR = 1.16, 95% CI: 1.07, 1.25). More oncologist visits were associated with patients visiting a female oncologist (VRR = 1.20, 95% CI: 1.09, 1.33) and fewer visits were associated with patients visiting an oncologist who practiced outside of a regional cancer center (VRR = 0.67, 95% CI: 0.58, 0.77). Conclusions: Oncology visits of young breast cancer survivors after 5-year survival were associated with oncologist factors indicating that prolonged oncology follow-up in breast cancer survivors may be driven by practice patterns rather than patients’ needs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".