Rare, Serious, and Comprehensively Described Suspected Adverse Drug Reactions Reported by Surveyed Healthcare Professionals in Uganda
Bibliographic record
Abstract
BACKGROUND: Lack of adequate detail compromises analysis of reported suspected adverse drug reactions (ADRs). We investigated how comprehensively Ugandan healthcare professionals (HCPs) described their most recent previous-month suspected ADR, and determined the characteristics of HCPs who provided comprehensive ADR descriptions. We also identified rare, serious, and unanticipated suspected ADR descriptions with medication safety-alerting potential. METHODS: During 2012/13, this survey was conducted in purposively selected Ugandan health facilities (public/private) including the national referral and six regional referral hospitals representative of all regions. District hospitals, health centres II to IV, and private health facilities in the catchment areas of the regional referral hospitals were conveniently selected. Healthcare professionals involved in prescribing, transcribing, dispensing, and administration of medications were approached and invited to self-complete a questionnaire on ADR reporting. Two-thirds of issued questionnaires (1,345/2,000) were returned. RESULTS: Ninety per cent (241/268) of HCPs who suspected ADRs in the previous month provided information on five higher-level descriptors as follows: body site (206), drug class (203), route of administration (127), patient age (133), and ADR severity (128). Comprehensiveness (explicit provision of at least four higher-level descriptors) was achieved by at least two-fifths (46%, 124/268) of HCPs. Received descriptions were more likely to be comprehensive from HCPs in private health facilities, regions other than central, and those not involved in teaching medical students. Overall, 106 serious and 51 rare previous-month suspected ADRs were described. The commonest serious and rare ADR was Stevens-Johnson syndrome (SJS); mostly associated with oral nevirapine or cotrimoxazole, but haemoptysis after diclofenac analgesia and paralysis after quinine injection were also described. CONCLUSION: Surveyed Ugandan HCPs who had suspected at least one ADR in the previous month competently provided comprehensive ADR descriptions: more, indeed, than are received per annum nationally. Properly analyzed, and with local feed-back, voluntary ADR reports by HCPs could be an essential alerting tool for identifying rare and serious suspected ADRs in Uganda.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".