Patterns of Cancer Centre Follow-Up Care for Survivors of Breast, Colorectal, Gynecologic, and Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Rising demand on cancer system resources, alongside mounting evidence that demonstrates the safety and acceptability of primary care-led follow-up care, has resulted in some cancer centres discharging patients back to primary care after treatment. At the same time, the ways in which routine cancer follow-up care is provided across Canada continue to vary widely. The objectives of the present study were to investigate patterns of routine follow-up care at a cancer centre for breast, colorectal, gynecologic, and prostate cancer survivors; factors associated with receipt of follow-up care at a cancer centre; and changes in follow-up care at a cancer centre over time. METHODS: = 12,267) and developed decision rules to differentiate routine from non-routine visits during the follow-up care period (commencing 1 year after diagnosis). Descriptive statistics were computed to describe the patterns of routine follow-up care at a cancer centre. Negative binomial regression was used to examine factors associated with visits made and changes over time. RESULTS: Nearly half the survivors (48.4%) had at least 1 follow-up visit to the cancer centre, with variation by disease site (range: 30.2%-62.4%). Disease site and stage at diagnosis were associated with receipt of follow-up care at a cancer centre. For instance, compared with breast cancer survivors, survivors of gynecologic cancer had more visits [incidence rate ratio (irr): 1.48; 95% confidence interval (ci): 1.34 to 1.64], and survivors of colorectal cancer had fewer visits (irr: 0.45; 95% ci: 0.40 to 0.51). Year of diagnosis was associated with follow-up at a cancer centre, with each successive calendar year being associated with an 8% increase in visits made (irr: 1.08; 95% ci: 1.07 to 1.10). CONCLUSIONS: Despite evidence that follow-up care can be effectively and safely delivered in primary care, and despite intensifying demands on oncology services, many survivors continue to receive routine follow-up care at a cancer centre.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".