Effects of patient decision aids in patients with type 2 diabetes mellitus: A systematic review and meta‐analysis
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: This study aimed to systematically evaluate the effectiveness of patient decision aids on knowledge, decisional conflict and decisional self-efficacy outcomes in patients with diabetes. METHODS: A comprehensive database search was performed using the Web of Science, Cochrane Library, PubMed, Embase, PsycINFO (Ovid), CINAHL (EBASCO), CNKI, VIP, Wan Fang Database and the Ottawa Decision Aid Library Inventory (http://decisionaid.ohri.ca/index.html) from inception to 13 October 2019. Two reviewers independently searched databases, screened articles, extracted data and evaluated the risk bias of included studies. Then Rev Man 5.3 software was adopted for statistical analysis. RESULTS: Ten articles containing 1,452 people with diabetes were selected. The results of meta-analysis showed that patient decision aids had a positive effect on reducing decisional conflict and improving decisional self-efficacy among patients with type 2 diabetes. Meanwhile, this article also revealed that patient decision aids have beneficial short-term effects on improving knowledge, but there was no significant long-term effect. CONCLUSION: Patient decision aids are capable of becoming support tools to improve shared decision making. Further implementation studies are required to transform patient decision aids tools into clinical practice.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it