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Record W2339384960 · doi:10.1093/cid/civ959

HIV Postexposure Prophylaxis Starter Packs Were Not Designed to Help or Hinder Adherence

2015· letter· en· W2339384960 on OpenAlexaff
Rudy Zimmer

Bibliographic record

VenueClinical Infectious Diseases · 2015
Typeletter
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineStarterHuman immunodeficiency virus (HIV)SidaPost-exposure prophylaxisPre-exposure prophylaxisIntensive care medicineVirologyViral diseaseImmunologyFood science

Abstract

fetched live from OpenAlex

To the Editor—It was concerning to read the conclusions of the systematic review by Ford and colleagues [1] promoting full 28-day starts of human immunodeficiency virus (HIV) postexposure prophylaxis (PEP) over traditional starter packs usually containing 1–7 days' worth of drugs dispensed at the initial medical visit, often within a busy emergency department (ED). The premise appears to be based on an analysis trying to suggest that starter packs do not promote adherence and may even undermine it [1]. The review also appears to be the justification for the formal opinion expressed in Section 4.6 of the recent World Health Organization (WHO) HIV PEP guidelines [2]. However, the premise is based on a flawed assumption regarding the primary purpose of starter packs. Starter packs are used to promote early access at multiple sites within a local healthcare system; to ensure continuity of care during follow-up; and to test the exposed client's tolerance and adherence without significantly wasting expensive HIV drugs. They were never designed to improve adherence, which is perhaps one reason why the authors were unable to find any published studies that directly tested their hypothesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.025
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0250.017
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.093
GPT teacher head0.412
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractno

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