Prevalence of pain in patients with HIV/AIDS: A cross-sectional survey in a South Indian state
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".