Original Contribution Cost Effectiveness of a Survivorship Care Plan for Breast Cancer Survivors
Bibliographic record
Abstract
Purpose: Survivorship care plans (SCPs) are recommended for patients who have completed primary treatment and are tran-sitioning to routine follow-up care. However, SCPs may be costly, and their effectiveness is unproven. The study objective was to assess the cost effectiveness of an SCP for breast cancer survivors transitioning to routine follow-up care with their own primary care physician (PCP) using data from a recent random-ized controlled trial (RCT). Methods: Resource use and utility data for 408 patients with breast cancer enrolled in the RCT comparing an SCP with stan-dard care (no SCP) were used. The intervention group received a 30-minute educational session with a nurse and their SCP, and their PCPs received the SCP plus a full guideline on follow-up. Analysis assessed the societal costs and quality-adjusted life years (QALYs) for the intervention group and the control group over the 2-year follow-up of the RCT. Uncertainty concerning cost effectiveness was assessed through nonparametric boot-strapping and deterministic sensitivity analysis. Results: The no-SCP group had better outcomes than the SCP group: total costs per patient were lower for standard care (Canadian $698 v $765), and total QALYs were almost equiva-lent (1.42 for standard care v 1.41 for the SCP). The probability that the SCP was cost effective was 0.26 at a threshold value of a QALY of $50,000. A variety of sensitivity analyses did not change the conclusions of the analysis. Conclusion: This SCP would be costly to introduce and would not be a cost effective use of scarce health care resources.
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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 | 0.012 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".