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Record W4384393095 · doi:10.1093/ofid/ofad350

Global Prevalence of Chronic Pain in Women with HIV: A Systematic Review and Meta-analysis

2023· review· en· W4384393095 on OpenAlexafffund
Tetiana Povshedna, Shayda A. Swann, Sofia L. A. Levy, Amber R Campbell, Manon Choinière, Madéleine Durand, Colleen Price, Prubjot Gill, Melanie C. M. Murray, Hélène C. F. Côté

Bibliographic record

VenueOpen Forum Infectious Diseases · 2023
Typereview
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsCanadian AIDS SocietyUniversité de MontréalB.C. Women's Hospital & Health CentreWomen's Health Research InstituteUniversity of British Columbia
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchCanadian HIV Trials Network, Canadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsMedicineMeta-analysisHuman immunodeficiency virus (HIV)Chronic painPhysical therapyInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

Abstract Chronic pain is common among people with human immunodeficiency virus (HIV) and detrimental to quality of life and overall health. It is often underdiagnosed, undertreated, and frankly dismissed in women with HIV, despite growing evidence that it is highly prevalent in this population. Thus, we conducted a systematic review and meta-analysis to estimate the global prevalence of chronic pain in women with HIV. The full protocol can be found on PROSPERO (identifier CRD42022301145). Of the 2984 references identified in our search, 36 were included in the systematic review and 35 in the meta-analysis. The prevalence of chronic pain was 31.2% (95% confidence interval [CI], 24.6%–38.7%; I2 = 98% [95% CI, 97%–99%]; P < .0001). In this global assessment, we found a high prevalence of chronic pain among women with HIV, underscoring the importance of understanding the etiology of chronic pain, identifying effective treatments, and conducting regular assessments in clinical practice.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.751
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0100.001
Bibliometrics0.0000.002
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 head0.057
GPT teacher head0.388
Teacher spread0.331 · 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.

Study designMeta-analysis
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

Citations13
Published2023
Admission routes2
Has abstractyes

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