What Is the Effectiveness of Patient Decision Aids for Cancer-Related Decisions? A Systematic Review Subanalysis
Bibliographic record
Abstract
PURPOSE: To determine the effectiveness of patient decision aids when used with patients who face cancer-related decisions. PATIENTS AND METHODS: Two reviewers independently screened the 105 trials in the original 2017 Cochrane review to identify eligible trials of patient decision aids across the cancer continuum. Primary outcomes were attributes of the choice and decision-making process. Secondary outcomes were patient behavior and health system effects. A meta-analysis was conducted for similar outcome measures. RESULTS: Forty-six trials evaluated patient decision aids for cancer care, including 27 on screening decisions (59%), 12 on treatments (26%), four on genetic testing (9%), and three on prevention (6%). Common decisions were aboutprostate cancer screening (30%), colorectal cancer screening (22%), breast cancer treatment (13%), and prostate cancer treatment (9%). Compared with the control groups (usual care or alternative interventions), the patient decision aid group improved the match between the chosen option and the features that mattered most to the patient as demonstrated by improved knowledge (weighted mean difference, 12.88 of 100; 95% CI, 9.87 to 15.89; 24 trials), accurate risk perception (risk ratio [RR], 1.77; 95% CI, 1.22 to 2.56; six trials), and value-choice agreement (RR, 2.76; 95% CI, 1.57 to 4.84; nine trials). Compared with controls, the patient decision aid group improved the decision-making process with decreased decisional conflict (weighted mean difference, -9.56 of 100; 95% CI, -13.90 to -5.23; 12 trials), reduced clinician-controlled decision making (RR, 0.57; 95% CI, 0.41 to 0.79; eight trials), and fewer patients being indecisive (RR, 0.59; 95% CI, 0.45 to 0.78; nine trials). CONCLUSION: Patient decision aids improve the attributes of the choice made and decision-making process for patients who face cancer-related decisions.
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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 | 0.052 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".