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Record W4385407095 · doi:10.1007/s10461-023-04121-0

Systematic Review and Meta-analysis of Linkage to HIV Care Interventions in the United States, Canada, and Ukraine (2010–2021)

2023· review· en· W4385407095 on OpenAlexaboutno aff
Julie H. Levison, Paola Del Cueto, Jaime Vladimir Mendoza, Dina Ashour, Melis Lydston, Kenneth A. Freedberg, Fatma M. Shebl

Bibliographic record

VenueAIDS and Behavior · 2023
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesHarvard University Center for AIDS Research
KeywordsMedicinePsychological interventionCINAHLLinkage (software)Meta-analysisCochrane LibraryIncidence (geometry)Cumulative incidenceFamily medicineConfidence intervalDemographyInternal medicineNursingGeneticsBiologyCohort

Abstract

fetched live from OpenAlex

We conducted a systematic review and meta-analysis of interventions targeting linkage to HIV care in the US, Canada, and Europe. We searched six databases (PubMed, Embase, Cochrane Library, Web of Science and CINAHL). Inclusion criteria were English language studies in adults in the US, Canada, or Europe, published January 1, 2010 to January 1, 2021. We synthesized interventions by type and linkage to care outcome. The outcome was cumulative incidence of 3-month linkage. We estimated cumulative incidence ratios of linkage with 95% confidence intervals (CIs). We screened 945 studies; 13 met selection criteria (n = 1 from Canada, n = 1 from Ukraine, n = 11 from the US) and were included after full text review (total 37,549 individuals). The cumulative incidence of 3-month linkage in the intervention group was 0.82 (95% CI 0.68-0.94) and control group 0.71 (95% CI 0.50-0.90); cIR of linkage for intervention versus control was 1.30 (95% CI 1.13, 1.49). Interventions to improve linkage to care after HIV diagnosis warrant further attention.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.849
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.146
GPT teacher head0.429
Teacher spread0.283 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations4
Published2023
Admission routes1
Has abstractyes

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