Optimizing HIV therapy. A consensus project on differences between cytidine analogues and regime compactness.
Bibliographic record
Abstract
OBJECTIVES: The identification of the most effective HAART regimens in different clinical settings is still an issue. The aim of the study was to analyze how the compactness of HAART regimens is perceived and if differences between lamivudine (3TC) and emtricitabine (FTC) do exist according to a panel of Italian HIV/AIDS clinicians, using the Delphi method. METHODS: The Delphi technique relies on a structured group of participants to reach a consensus on debated topics. Issues related to the compactness of HAART and to 3TC / FTC features were identified and proposed to randomly- selected 80 HIV/AIDS Italian clinicians by questionnaires. Questionnaires were administered in two rounds. The steering board of the project discussed the answers after each round to reformulate or to draw conclusions, respectively. RESULTS: Participants agreed that the compactness of HAART may influence adherence and outcome in many clinical conditions. Moreover, differences between FTC and 3TC were acknowledged with respect to pharmacokinetics, genetic barrier, antiviral potency, and resistance mutations arising at virologic failure. CONCLUSIONS: The Delphi method proved useful to focus on and gauge the relevance of issues such differences between the two cytidine analogues (FTC and 3TC) and the overall compactness of HAART combinations in HIV/ AIDS therapy.
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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 | 0.083 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".