Choosing Wisely in Physical Medicine and Rehabilitation
Bibliographic record
Abstract
ABSTRACT: Choosing Wisely Canada aims to reduce potentially harmful or unnecessary diagnostic investigations and practices in healthcare delivery. A committee of the Canadian Association of Physical Medicine & Rehabilitation surveyed the general membership seeking suggestions on new or revised Physical Medicine & Rehabilitation Choosing Wisely Canada recommendations. Draft recommendations were revised and refined with an emphasis on resource stewardship and alignment with the Choosing Wisely Canada mission. The updated 2023 Choosing Wisely Canada recommendations for physical medicine and rehabilitation are to avoid: (1) investigating and treating asymptomatic bacteriuria in patients with neurogenic bladder; (2) recommending more than a brief period of physical and cognitive rest after mild traumatic brain injury; (3) starting opioid treatment for chronic noncancer pain without exhausting other approaches; (4) ordering diagnostic imaging for low back pain in the absence of red flags; (5) repeating injections without evaluating patients' responses to them; and (6) recommending carpal tunnel release without first confirming nerve entrapment with electrodiagnostic studies or ultrasonography. We present unique implementation tools to equip practitioners with quality improvement strategies to adopt these recommendations into their practices. Future work will include developing recommendations that consider planetary health co-benefits, creating knowledge translation tools, and assessing the impact of recommendation adoption into clinical practice.
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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".