Evaluation of HIV Drug Interaction Web Sites
Bibliographic record
Abstract
BACKGROUND: Clinicians frequently consult HIV drug interaction Web sites of unknown quality. OBJECTIVE: To systematically review and identify HIV drug interaction Web sites of high quality and usefulness for healthcare professionals. METHODS: Relevant Web sites were identified through a structured search on commonly used search engines. An assessment tool containing 4 domains (content, reliability, access restrictions, ease of navigation) was developed. English and French Web sites were selected for review if they included HIV drug interaction information directed to healthcare professionals. Web sites were excluded if antiretroviral interaction data were not available or were out of date. Commercial online databases and sites that required payment were not included. Seventeen HIV pharmacists from across Canada participated in the review. The Web sites were ranked with total mean scores. Mean scores for each domain were then analyzed. Interrater agreement and ANOVA using the rater as a covariate were determined. RESULTS: Nine Web sites met the criteria for review. Web sites from Toronto General Hospital (Canada), HIVinSite (beta version) (US), and the University of Liverpool (UK) ranked highest for total mean scores and for content. Other Web sites were found to be reliable, accessible, and easy to navigate; however, they did not consistently include unpublished data or data on herbal preparations, recreational drugs, or multiple interactions. CONCLUSIONS: Three HIV interaction Web sites of high quality were identified that can be valuable tools for HIV and non-HIV health-care professionals. Regular reviews are necessary in order to keep pace with the growing body of HIV interaction data and the constant evolution of Web sites.
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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 | 0.056 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".