An online individualised patient decision aid improves the quality of decisions in patients considering total knee arthroplasty in routine care: A randomized controlled trial
Bibliographic record
Abstract
Objective: The objective of this study was to evaluate the effectiveness of an online patient decision aid with individualised potential outcomes of surgery, on the quality of decisions for knee replacement surgery in routine clinical care. Design: A pragmatic Randomized Controlled Trial (RCT) in patients considering total knee replacement at a high-volume orthopedic clinic. Patients were randomized at their routine online pre-surgical assessment to either complete a decision aid or not. At their consultation, those in the intervention arm had a surgeon report summarizing the decision aid results. The primary outcome was decision quality, defined as being knowledgeable and choosing the option that matched informed treatment preferences. Multivariate logistic and linear regression analysis was conducted to consider surgeon level clustering and baseline differences between study arms. Results: Of 163 patients randomized, 155 completed post-surgical surveys and were included in the analysis. The average patient was aged 65 years, obese and had moderate to severe osteoarthritis symptoms at baseline. Patients in the intervention arm had a higher odds of making a quality decision (Odds Ratio = 2.08, 95% CI: 1.08 to 4.02), predominantly through increased knowledge. Conclusions: This study supports the benefit of a decision aid in combination with a surgeon report to significantly improve decision quality in routine care. While the independent contribution of tailoring the decision aid to patient baseline characteristics and including a surgeon report remains unclear, we demonstrated the feasibility of integrating the decision aid into an online pre-surgical assessment in routine clinical care.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 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".