Effects of patient decision aids used pre-consult or in-consult on patient-clinician communication - secondary analysis of a systematic review with meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patient decision aids (PtDAs) provide benefits and risks of options for a specific decision, and help patients clarify their values. This study examined whether the timing of PtDA use—before (pre-consult) or during (in-consult) the clinical encounter—affects patient-clinician communication. METHODS: We conducted a secondary analysis of 209 randomized controlled trials (RCTs) in the 2024 Cochrane review of PtDAs compared to usual care. Eligible studies measured patient-clinician communication using observer-, patient- or clinician-reported instruments. RESULTS: Thirty-six RCTs met inclusion criteria, reporting on communication outcomes: 21 evaluated pre-consult PtDAs and 15 in-consult PtDAs. Pre-consult PtDAs commonly addressed screening and treatment decisions, often using digital formats. In-consult PtDAs focused on treatment and were mostly paper-based. For pre-consult PtDAs, 68.6% of patients discussed the decision with their clinician versus 50.2% in usual care (p < 0.001), though no difference was found for patient-reported SDM-Q-9 scores. In-consult PtDAs significantly improved communication measured by the observer-rated OPTION12 instrument. CONCLUSIONS: The effects of PtDAs varied by timing and measurement approach, with in-consult PtDAs potentially offering more structured support for shared decision making. No studies directly compared pre- and in-consult PtDAs. Future research should directly compare these approaches and use consistent communication measures.
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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 | 0.036 | 0.091 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.076 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".