A Goal Without a Plan Is Just a Wish—Creating a Personalized Aftercare Plan for Breast Cancer Patients Supported by the Breast Cancer Aftercare Decision Aid
Bibliographic record
Abstract
Aftercare plans can support breast cancer patients' self-management after curative treatment but are often not personalized and limitedly applied by healthcare practitioners (HCPs). This study aimed to develop a tool integrating information provision and assessment of patients' goals and needs, to support the creation and application of a personalized aftercare plan. A multidisciplinary workgroup guided the development by defining the target audience, scope and purpose. Needs of 18 patients and 15 HCPs were assessed to determine the tool's content and format. Usability tests of a prototype among 7 patients and 10 HCPs informed improvements and finalization. The tool, called 'Breast Cancer Aftercare Decision Aid' (BC-ADA), provides information on potential effects of cancer and support options on five domains: physical wellbeing, emotions, relationships, regaining trust and return to daily routine. Patients can indicate which domain(s) they wish to improve, what resources they have and where additional help is needed. Based on their answers, patients can create an aftercare plan together with the HCP, including personal goals, specific actions and agreements on follow-up. Usability and acceptability were positively evaluated by both patients and HCPs. The BC-ADA seems promising in supporting personalized aftercare decision-making and is currently being tested in the NABOR-study in Dutch hospitals.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".