Problem with patient decision aids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Patient decision aids are evidence-based tools designed to help patients make specific and deliberated choices among healthcare options. Research shows that patient decision aids increase knowledge, accuracy of risk perceptions, alignment of care with patient values and preferences, and patient involvement in decision making. Some patient decision aids can reduce the use of invasive and potentially low-value procedures. On this basis, clinical practice guidelines and international organisations have begun to recommend the use of patient decision aids and shared decision making as a strategy to reduce medical overuse. Although patient decision aids hold promise for improving healthcare, there are fundamental issues with patient decision aids that need to be addressed before further progress can be made. The problems with patient decision aids are: (1) Guidelines for developing patient decision aids may not be sufficient to ensure developers select the best available evidence and present it appropriately; (2) Biased presentation of low-certainty evidence is common and (3) Biased presentation of low-certainty evidence is misleading, and could inadvertently support, low-value care. We explore these issues in the article and present a case study of online patient decision aids for musculoskeletal pain. We suggest ways to ensure patient decision aids help patients understand the evidence and, where possible, support high-quality care.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it