Expert Involvement and Adherence to Medical Evidence in Medical Mobile Phone Apps: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Both clinicians and patients use medical mobile phone apps. Anyone can publish medical apps, which leads to contents with variable quality that may have a serious impact on human lives. We herein provide an overview of the prevalence of expert involvement in app development and whether or not app contents adhere to current medical evidence. OBJECTIVE: To systematically review studies evaluating expert involvement or adherence of app content to medical evidence in medical mobile phone apps. METHODS: We systematically searched 3 databases (PubMed, The Cochrane Library, and EMBASE), and included studies evaluating expert involvement or adherence of app content to medical evidence in medical mobile phone apps. Two authors performed data extraction independently. Qualitative analysis of the included studies was performed. RESULTS: Based on inclusion criteria, 52 studies were included in this review. These studies assessed a total of 6520 apps. Studies dealt with a variety of medical specialties and topics. As much as 28 studies assessed expert involvement, which was found in 9-67% of the assessed apps. Thirty studies (including 6 studies that also assessed expert involvement) assessed adherence of app content to current medical evidence. Thirteen studies found that 10-87% of the assessed apps adhered fully to the compared evidence (published studies, recommendations, and guidelines). Seventeen studies found that none of the assessed apps (n=2237) adhered fully to the compared evidence. CONCLUSIONS: Most medical mobile phone apps lack expert involvement and do not adhere to relevant medical evidence.
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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 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".