MétaCan
Menu
Back to cohort
Record W2075372225 · doi:10.4103/0973-1075.53550

Prevalence of pain in patients with HIV/AIDS: A cross-sectional survey in a South Indian state

2009· article· en· W2075372225 on OpenAlexaboutno aff
Shoba Nair, TheophinRegina Mary, S Prarthana, Preethy Harrison

Bibliographic record

VenueIndian Journal of Palliative Care · 2009
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCross-sectional studyQuality of life (healthcare)Human immunodeficiency virus (HIV)Physical therapyDiseaseAnalgesicBrief Pain InventoryMcGill Pain QuestionnaireNeuropathic painInternal medicineChronic painFamily medicineVisual analogue scalePsychiatryAnesthesia

Abstract

fetched live from OpenAlex

OBJECTIVES: Primary - To measure the prevalence of pain in HIV/AIDS with patients. Secondary - To assess the type, site, severity, management of pain and impact of pain on quality of life in these patients. DESIGN: Multicentre cross-sectional survey (This paper is a pilot study). SETTINGS: ART centre at St. John's Medical College Hospital, Bangalore and Snehadan, A supportive and care centre for HIV/ AIDS patients at Bangalore. MATERIALS AND METHODS: Data sheet, Brief pain inventory and Short - Form McGill pain questionnaire. SUBJECTS: This is an ongoing study and the pilot study includes 140 HIV/AIDS patients in different stages of the disease. RESULTS: About 66.7% (28/42) in-patients and 24.5% (24/98) out-patients complained of pain. Of the 52 patients who reported pain, 32% (14/52) reported neuropathic pain and 68% (38/52) reported noci-ceptive pain. Headache was most common followed by pain in the soles of feet and low back. Only 26.9% (17/52) received any form of analgesic. Pain severity significantly affects the quality of life. CONCLUSIONS: Pain is a common and debilitating symptom of HIV/AIDS. It is however, under-estimated and under treated.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.323
Teacher spread0.298 · 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 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

Citations38
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueIndian Journal of Palliative CareSame topicHIV-related health complications and treatmentsFrench-language works237,207