Patient decision aids for cancer treatment
Bibliographic record
Abstract
BACKGROUND: Although patient decision aids (pDAs) are effective, widespread use of pDAs for cancer treatment has not been achieved. The objectives of this study were to perform a systematic review to identify alternate types of decision support interventions (DSIs) for cancer treatment and a meta-analysis to compare the effectiveness of these DSIs to pDAs. METHODS: The inclusion criteria for the study were: 1) all published studies using a randomized, controlled trial design, and 2) DSIs involving treatment decision-making for breast, prostate, colorectal, and/or lung cancer. For this analysis, DSIs were classified as pDAs if: 1) one reported outcome measure mapped onto the International Patient Decision Aids Standards Collaboration effectiveness criterion, and 2) the DSI was evaluated relative to standard consultation. Random effects models were used to compare the effectiveness of pDAs relative to other identified DSIs for reported outcomes. RESULTS: A total of 71 studies were reviewed, and 24 met the inclusion criteria. Overall, there were no significant differences in knowledge, satisfaction, anxiety, or decisional conflict scores between pDAs and other DSIs. CONCLUSIONS: This study showed that the effectiveness of other DSIs, including question prompt lists and audiorecording of the consultation, is similar to pDAs. This is important because it may be that these less complex DSIs may be all that is necessary to achieve similar outcomes as pDAs for cancer treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".