Effectiveness of Mobile Medical Applications Making Sure Medication Safety on Chronic Disease Patients: A Systematic review and Meta analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Along with the rapid development of global aging society, the mobile and health digital market has expanded a lot. There are numerous numbers of Mobile Medical Applications emerged on the Internet market, aiming to help patients with chronic diseases achieve the goal of medication safety OBJECTIVE Based on the medication safety action proposed by WHO, the effect of Mobile Medical Applications on the medication safety with chronic disease patients was explored from three aspects: whether Mobile Medical Applications can improve the willingness to report adverse drug events, improve patients' medication adherence and reduce medication errors, so we want to verify our hypothesis via systematic review and meta-analysis. METHODS We strictly followed PRISMA for meta-analysis, including literature search, inclusion and exclusion, data extraction, quality assessment, statistical analysis and subgroup analysis. RESULTS Our study ultimately included 8 studies from 5 countries (China, U.S, France, Canada, Spain)and time from 2014 to 2021, which Mobile Medical Applications could increase ADE reporting willingness [RR= 2.59,95%CI (1.26-5.30),P＜0.01] and significantly improve medication adherence[RR= 1.17,95%CI (1.04-1.31),P＜0.01], but had little effect on reducing medication errors[RR= 0.41,95%CI (0.13-1.33),P＞0.05]. CONCLUSIONS To explore the associated reasons for the low willingness to report ADE and encourage the use of comprehensive tools to assess patient medication adherence, and analyze potential reasons why Mobile Medical Applications does not reduce medication errors. CLINICALTRIAL This systematic review has registered in PROSPERO(an international database of prospectively registered systematic reviews), and register number is CRD42022322072.
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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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".