Mobile App Design, Development, and Publication for Adverse Drug Reaction Assessments of Causality, Severity, and Preventability
Bibliographic record
Abstract
BACKGROUND: Adverse drug reactions (ADRs) cause significant morbidity and mortality. Improved assessment of ADRs to identify the causal relationship, the severity, and the preventability will aid ADRs prevention or reduce patient burden. OBJECTIVE: The aim of this study was to develop mobile apps in assisting clinical decision in ADR assessments of causality, severity, and preventability using validated tools. The usability of the apps was assessed. METHODS: We designed mobile apps using validated assessment tools for ADRs. They are the Liverpool ADRs Causality Assessment Tool, Hartwig's Severity Assessment Scale, and the Modified Schumock and Thronton Preventability Scale. The apps were named "Adverse Drug ReactionCausality," "Adverse Drug ReactionSeverity," and "Adverse Drug RxnPreventability." A survey was conducted using the System Usability Scale (SUS) to assess the usability of the developed apps among health care professionals. RESULTS: These apps are available for download through Google Play Store for free since January 2015. From the survey, the mean SUS score was 70.9 based on 26 responses from the pediatric ward of Hospital Ampang, Malaysia. CONCLUSIONS: The developed apps received an overall acceptable usability among health care professionals. The usage of these apps will improve detection, assessment, and avoidance of future ADRs. They will also contribute to future research on ADRs, thus increasing drug safety.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".