Targeted Spontaneous Reporting: Assessing Opportunities to Conduct Routine Pharmacovigilance for Antiretroviral Treatment on an International Scale
Bibliographic record
Abstract
INTRODUCTION: Targeted spontaneous reporting (TSR) is a pharmacovigilance method that can enhance reporting of adverse drug reactions related to antiretroviral therapy (ART). Minimal data exist on the needs or capacity of facilities to conduct TSR. OBJECTIVES: Using data from the International epidemiologic Databases to Evaluate AIDS (IeDEA) Consortium, the present study had two objectives: (1) to develop a list of facility characteristics that could constitute key assets in the conduct of TSR; (2) to use this list as a starting point to describe the existing capacity of IeDEA-participating facilities to conduct pharmacovigilance through TSR. METHODS: We generated our facility characteristics list using an iterative approach, through a review of relevant World Health Organization (WHO) and Uppsala Monitoring Centre documents focused on pharmacovigilance activities related to HIV and ART and consultation with expert stakeholders. IeDEA facility data were drawn from a 2009/2010 IeDEA site assessment that included reported characteristics of adult and pediatric HIV care programs, including outreach, staffing, laboratory capacity, adverse event monitoring, and non-HIV care. RESULTS: A total of 137 facilities were included: East Africa (43); Asia-Pacific (28); West Africa (21); Southern Africa (19); Central Africa (12); Caribbean, Central, and South America (7); and North America (7). Key facility characteristics were grouped as follows: outcome ascertainment and follow-up; laboratory monitoring; documentation-sources and management of data; and human resources. Facility characteristics ranged by facility and region. The majority of facilities reported that patients were assigned a unique identification number (n = 114; 83.2 %) and most sites recorded adverse drug reactions (n = 101; 73.7 %), while 82 facilities (59.9 %) reported having an electronic database on site. CONCLUSION: We found minimal information is available about facility characteristics that may contribute to pharmacovigilance activities. Our findings, therefore, are a first step that can potentially assist implementers and facility staff to identify opportunities and leverage their existing capacities to incorporate TSR into their routine clinical programs.
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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 | 0.207 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".