MétaCan
Menu
Back to cohort
Record W2752917534 · doi:10.1111/hiv.12533

Out of focus: tailoring the cascade of care to the needs of women living with HIV

2017· review· en· W2752917534 on OpenAlexaff
Anna María Geretti, Mona Loutfy, Antonella d’Arminio Monforte, И. Б. Латышева, Janice Rymer, Marta Boffito

Bibliographic record

VenueHIV Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsWomen's College HospitalUniversity of Toronto
FundersGilead SciencesWorld Health Organization
KeywordsMedicinePsychosocialHuman immunodeficiency virus (HIV)Antiretroviral therapyPopulationHealth careGerontologyClinical trialFamily medicineEnvironmental healthPsychiatryViral loadPathologyEconomic growth

Abstract

fetched live from OpenAlex

Around half of the global adult HIV-positive population are women, yet historically women have been under-represented in clinical studies of antiretroviral therapy (ART) and there has been minimal exploration of gender-specific factors related to the response to and appropriateness of treatment choices in women living with HIV (WLWH). There are several key issues pertaining to the cascade of HIV care that make it important to differentiate WLWH from men living with HIV. Factors that are gender specific may impact on the status of WLWH, affecting access to diagnosis and treatment, optimal clinical management, ART outcomes, retention in care, and the overall long-term wellbeing of WLWH. In this review, we discuss the results of recently reported women-only clinical trials and highlight the key unmet needs of WLWH as they pertain to the cascade of HIV care across World Health Organization European Region countries. As significant knowledge gaps remain, the review identifies key areas where further research is required, in order to support improved management of WLWH and guide informed clinical decision-making, including addressing psychosocial factors as part of comprehensive care.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.875
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.083
GPT teacher head0.398
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 teacher head, not a consensus.

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

Citations15
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueHIV MedicineSame topicHIV/AIDS Research and InterventionsFrench-language works237,207