POPULATION-BASED ESTIMATES OF COMMUNITY-BASED ADVERSE DRUG REACTIONS (ADRS) IN THE KINGDOM OF SAUDI ARABIA
Bibliographic record
Abstract
Background: Adverse drug reactions (ADRs) represent important preventable causes of mortality, morbidity, hospitalization and increased healthcare costs. Traditionally, ADRs are studied in a clinical setting, but it is also important to estimate rates of ADRs in the community. The current study aimed to estimate population-based rates of ADRs in the community in the Kingdom of Saudi Arabia (KSA). Methods: A nationwide cross-sectional survey was conducted via registered pharmacists at community pharmacies across the 13 regions of KSA. The data were collected on an electronic online platform and included questions about participants’ demographics, health characteristics, experience with ADRs within the last 12 months and assess their knowledge about Saudi Food and Drug Authority (SFDA) reporting system. Results: Data collection was conducted between June and August 2018. Data from 5,228 surveys was analyzed. After weighting, the national annual rate of ADRs was estimated to be 28.00% (95% confidence interval 26.10% -30.00%). Of the respondents reporting an ADR, 371 (30.26%) reported that they were aware of the SFDA reporting system. Those who indicated they were aware, were asked if they had ever filed a report in the system; 53 (14.29%) said they had made a report. Conclusion: The results of this population-based estimates of community-based ADRs nationally in KSA showed that more than one quarter of the population had experienced ADR in the last 12 months. Future study is needed to better understand why these rates are higher in some regions than others, and what is needed to prevent high rates in subgroups such as women and those with chronic diseases.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".