Cost-effectiveness of a survivorship care plan for breast cancer survivors.
Bibliographic record
Abstract
6082 Background: Survivorship care plans (SCP) are recommended for patients who have completed primary treatment and are transitioning to routine follow-up care. However, SCPs may be costly and their effectiveness is unproven. The objective of this study was to assess the cost effectiveness of a SCP for breast cancer survivors transitioning to routine follow-up care with their own family physician (FP) using data from a recent randomized controlled trial (RCT). Methods: Analysis used resource use and utility data for 408 breast cancer patients enrolled in the RCT. In the intervention group, patients received a SCP consisting of: a 30-minute educational session with a nurse who reviewed a treatment summary, a patient-version of follow-up guidelines, brochures and information about local relevant supportive care resources; whilst FPs of intervention patients received a copy of all documents plus the full guideline and a reminder table of recommended follow-up visits and tests. Analysis assessed the societal costs and quality adjusted life years (QALYs) for both groups over the two year follow up of the RCT. Health care resources including physician visits and laboratory tests were weighted by appropriate Canadian unit costs. Analysis also considered costs to patients including travel and lost productivity assessed by the friction cost method. QALYs were assessed using the EQ5D questionnaire. Uncertainty concerning cost effectiveness was assessed through non parametric bootstrapping and deterministic sensitivity analysis. Results: The SCP was estimated to cost $59.96 per patient or $1.32 million per year in Canada given breast cancer incidence. The control group dominated the intervention group: as total costs per patient were lower for the control group ($736 versus $789) and total QALYs were higher (1.42 versus 1.41). The probability that the SCP was cost effective was 27% at a threshold value of a QALY of $50,000. Sensitivity analyses adopting a health care system perspective, using the human capital approach and including the cost of recurrences did not change the conclusions of the analysis. Conclusions: Based on the findings of this trial, SCPs would be costly to introduce and would not be cost effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.029 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".