Development of an evidence-based educational resource in oncology: ‘Living safely with bone metastases’
Bibliographic record
Abstract
To create an evidence-based patient education resource to better support cancer patients with bone metastases in carrying out safe movements during activities of daily living, to maintain their bone health and reduce the risk of fractures. A quality improvement project was conducted in three phases: Development of the Resource, Preliminary Feedback and Revision, and French Canadian Translation. The educational resource Living Safely with Bone Metastases focuses on safe movement, activities of daily living, and exercise, organized within the sections Move with care, Stay safe in different environments and Follow an exercise program prescribed by a physiotherapist. Translation yielded a Canadian French version Vivre en toute sécurité avec des métastases osseuses. Living Safely with Bone Metastases is an accessible online and paper resource for patients and healthcare professionals, in order to promote ongoing disease management of individuals with bone metastases. Cancer patients with bone metastases are at high risk of pathological fractures however resources on fracture prevention are lacking. Living Safely with Bone Metastases is an innovative health education resource that fills an important gap in oncology practice and has the potential to reduce the occurrence of fractures.
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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 | 0.065 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".