MétaCan
Menu
Back to cohort
Record W3037108259 · doi:10.1177/2325958220931735

Trends and Sex Differences in Access to HIV Care with Scale Up of National HIV Treatment Guidelines in Pune, India

2020· article· en· W3037108259 on OpenAlexaff
Priyanka Raichur, Sonali Salvi, Shashikala Sangle, Amol Chavan, Smita Nimkar, Gajanan Gawande, Bharat Bhushan Rewari, Jyoti S. Mathad, Katherine N. McIntire, Amita Gupta, Ivan Marbaniang, Vidya Mave

Bibliographic record

VenueJournal of the International Association of Providers of AIDS Care (JIAPAC) · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill University
FundersNational Institute of Allergy and Infectious DiseasesamfAR, The Foundation for AIDS Research
KeywordsMedicineHuman immunodeficiency virus (HIV)Antiretroviral therapyDemographyAntiretroviral treatmentMen who have sex with menFamily medicineViral load

Abstract

fetched live from OpenAlex

Test and treat is the current global standard, yet sex differences persist in access to HIV care. We assessed the differences in presentation and antiretroviral therapy (ART) uptake by sex and ART-eligibility period among ART-naive adults registered at a public ART center in India. Four ART eligibility periods were defined by programmatically determined CD4 criteria (periods I-IV: CD4 <200, <350, ≤500 cells/μL, and any CD4) between January 2005 and December 2017. Of 23 957 participants, 12 510 were male. Men consistently presented with lower median CD4 count (period I-IV, P < .05) and higher median age (period I-III, P < .001) than women. From period I to IV, median age increased in women ( P < .0001), ART initiation time decreased in both sexes ( P < .001), and median CD4 remained <200 cells/µL in men. Advanced HIV disease and increasing age at presentation are persistent sex-specific trends which warrant innovative HIV testing strategies in both sexes.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.351
Teacher spread0.315 · 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 designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the International Association of Providers of AIDS Care (JIAPAC)Same topicHIV/AIDS Research and InterventionsFrench-language works237,207