A systematic review of information in decision aids
Bibliographic record
Abstract
OBJECTIVE: We completed a systematic review of information reported as included in decision aids (DAs) for adult patients, to determine if it is complete, balanced and accurate. SEARCH STRATEGY: DAs were identified using the Cochrane Database of DAs and searches of four electronic databases using the terms: 'decision aid'; shared decision making' and 'patients'; 'multimedia or leaflets or pamphlets or videos and patients and decision making'. Additionally, publications reporting DA development and actual DAs that were reported as publicly available on the Internet were consulted. Publications were included up to May 2006. DATA EXTRACTION: Data were extracted on the following variables: external groups consulted in development of the DA, type of study used, categories of information, inclusion of probabilities, use of citation lists and inclusion of patient experiences. MAIN RESULTS: 68 treatment DAs and 30 screening DAs were identified. 17% of treatment DAs and 47% of screening DAs did not report any external consultation and, of those that did, DA producers tended to rely more heavily on medical experts than on patients' guidance. Content evaluations showed that (i) treatment DAs frequently omit describing the procedure(s) involved in treatment options and (ii) screening DAs frequently focus on false positives but not false negatives. About 1/2 treatment DAs reported probabilities with a greater emphasis on potential benefits than harms. Similarly, screening DAs were more likely to provide false-positive than false-negative rates. CONCLUSIONS: The review led us to be concerned about completeness, balance and accuracy of information included in DAs.
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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 | 0.051 | 0.237 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".