Herbal medicine use and linked suspected adverse drug reactions in a prospective cohort of Ugandan inpatients
Bibliographic record
Abstract
BACKGROUND: Clinical history-taking can be employed as a standardized approach to elucidate the use of herbal medicines and their linked suspected adverse drug reactions (ADRs) among hospitalized patients. We sought to identify herbal medicines nominated by Ugandan inpatients; compare nomination rates by ward and gender; confirm the herbs' known pharmacological properties from published literature; and identify ADRs linked to pre-admission use of herbal medicines. METHODS: Prospective cohort of consented adult inpatients designed to assess medication use and ADRs on one gynaecological and three medical wards of 1790-bed Mulago National Referral Hospital. Baseline and follow-up data were obtained on patients' characteristics, including pre-admission use of herbal medicines. RESULTS: Fourteen percent (26/191) of females in Gynaecology nominated at least one specific herbal medicine compared with 20 % (114/571) of inpatients on medical wards [20 % (69/343) of females; 20 % (45/228) of males]. Frequent nominations were Persea americana (30), Mumbwa/multiple-herb clay rods (23), Aloe barbadensis (22), Beta vulgaris (12), Vernonia amygdalina (11), Commelina africana (7), Bidens pilosa (7), Hoslundia opposita (6), Mangifera indica (4), and Dicliptera laxata (4). Four inpatients experienced 10 suspected ADRs linked to pre-admission herbal medicine use including Commelina africana (4), multiple-herb-mumbwa (1), or unspecified local-herbs (5): three ADR-cases were abortion-related and one kidney-related. CONCLUSIONS: The named herbal medicines and their nomination rates generally differed by specialized ward, probably guided by local folklore knowledge of their use. Clinical elicitation from inpatients can generate valuable safety data on herbal medicine use. However, larger routine studies might increase the utility of our method to assess herbal medicine use and detect herb-linked ADRs. Future studies should take testable samples of ADR-implicated herbal medicines for further analysis.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".