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Record W2944262929 · doi:10.1371/journal.pone.0214251

Effectiveness of symptom-based diagnostic HIV testing versus targeted and blanket provider-initiated testing and counseling among children and adolescents in Cameroon

2019· article· en· W2944262929 on OpenAlexaff
Habakkuk Azinyui Yumo, Rogers Ajeh, Marcus Beißner, Jackson Jr Nforbewing Ndenkeh, Isidore Sieleunou, Michael R. Jordan, Nadia A. Sam‐Agudu, Christopher Kuaban

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversité de Montréal
FundersNational Institute of Allergy and Infectious DiseasesCenter for International HealthElse Kröner-Fresenius-StiftungDivision of Intramural Research, National Institute of Allergy and Infectious DiseasesCity University of New York
KeywordsMedicineHuman immunodeficiency virus (HIV)Diagnostic testBlanketFamily medicineClinical psychologyPediatrics

Abstract

fetched live from OpenAlex

OBJECTIVES: The concurrent implementation of targeted (tPITC) and blanket provider-initiated testing and counselling (bPITC) is recommended by the World Health Organization (WHO) for HIV case-finding in generalized HIV epidemics. This study assessed the effectiveness of this intervention compared to symptom-based diagnostic HIV testing (DHT) in terms of HIV testing uptake, case detection and antiretroviral therapy (ART) enrollment among children and adolescents in Cameroon, where estimated HIV prevalence is relatively low at 3.7%. METHODS: In three hospitals where DHT was the standard practice before, tPITC and bPITC were implemented by inviting HIV-positive parents in care at the ART clinics to have their biological children (6 weeks-19 years) tested for HIV (tPITC). Concurrently, at the outpatient departments, similarly-age children/adolescents were systematically offered HIV testing via accompanying parents/guardians. The mean monthly number of children tested for HIV, identified HIV-positive and ART-enrolled were used to compare the outcomes of different HIV testing strategies before and after the intervention. RESULTS: In comparing DHT to bPITC, there was a significant increase in the mean monthly number of children/adolescents tested for HIV (223.0 vs 348.3, p = 0.0073), but with no significant increase in the mean monthly number of children/adolescents: testing HIV-positive (10.5 vs 9.7, p = 0.7574) and ART- enrolled (7.3 vs 6.3, p = 0.5819). In comparing DHT to tPITC, there was no significant difference in the mean monthly number of children/adolescents: tested for HIV (223 vs 193.8, p = 0.4648); tested HIV-positive (10.5 vs 10.6, p = 0.9544), and ART-enrolled (7.3 vs 5.8, p = 0.4672). When comparing DHT versus bPITC+tPITC, there was a significant increase in the mean monthly number of children/adolescents: tested for HIV (223.0 to 542.2, p<0.0001), testing HIV-positive (10.5 vs 20.3, p = 0.0256), and ART-enrolled (7.3 vs 12.2, p = 0.0388). CONCLUSIONS: These findings suggest that concurrent implementation of bPITC+tPITC was more effective compared to DHT in terms of HIV testing uptake, case detection and ART enrolment. However, considering that DHT and bPITC had comparable outcomes with regards to case detection and ART enrolment, bPITC+tPITC may not be efficient. Thus, this finding does not support concurrent bPITC+tPITC implementation as recommended by WHO. Rather, continued DHT+tPITC could effectively and efficiently accelerate HIV case detection and ART coverage among children and adolescents in Cameroon and similar low-prevalence context.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.270
Teacher spread0.236 · 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 designNon-randomized trial
Domainnot available
GenreEmpirical

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

Citations17
Published2019
Admission routes1
Has abstractyes

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