20th Anniversary Update of the Ottawa Decision Support Framework: Part 2 Subanalysis of a Systematic Review of Patient Decision Aids
Bibliographic record
Abstract
Background. The Ottawa Decision Support Framework (ODSF) has guided the development of patient decision aids (PtDAs) for 20 years and needs updating across a range of decisions and hypothesized outcomes. Purpose. To determine the effectiveness of ODSF-developed PtDAs on hypothesized outcomes and to recommend framework changes. Data Source. A subanalysis of randomized controlled trials included in the 2017 Cochrane review of PtDAs comparing PtDAs to usual care in adults considering health treatment or screening decisions (searched to 2015). Study Selection. Trials in the original review that evaluated ODSF-developed PtDAs. Data Synthesis. Meta-analyses of ODSF outcomes with similar measurements and descriptions of other reported outcomes. Results. Of 105 trials, 24 evaluated ODSF-developed PtDAs. Compared with usual care, ODSF PtDAs improved knowledge (mean difference [MD] 13.85; 95% confidence interval [CI] 10.32−17.37; 14 trials), increased accurate risk perceptions (risk ratio [RR] 2.41; 95% CI 1.66−3.48; 7 trials), and increased congruence between informed values and chosen options (RR 1.32; 95% CI 1.09−1.59; 4 trials). They reduced perceived decisional needs as measured using the Decisional Conflict Scale (MD −5.92; 95% CI −8.58 to −3.26; 15 trials) and the proportion of undecided patients (RR 0.65; 95% CI 0.50−0.83; 13 trials). Non-ODSF PtDAs, designed with or without a specific framework, also outperformed usual care. Few ODSF trials measured secondary outcomes. Limitations. The included trials had heterogeneity. Conclusion. ODSF PtDAs address decisional needs and improve decision quality; the best indicator of addressing perceived uncertainty is “proportion undecided.” Secondary ODSF outcomes should be reduced to adherence to one’s chosen option and use/costs of health services, which warrant further research.
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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 | 0.107 | 0.254 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.045 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".