The Global Task Force for Chronic Pain in People with HIV (PWH): Developing a research agenda in an emerging field
Bibliographic record
Abstract
Chronic pain is a common comorbidity in people with HIV (PWH), with prevalence estimates of 25-85%. Research in this area is growing, but significant gaps remain. A Global Task Force of HIV experts was organized to brainstorm a scientific agenda and identify measurement domains critical to advancing research in this field. Experts were identified through literature searches and snowball sampling. Two online questionnaires were developed by Task Force members. Questionnaire 1 asked participants to identify knowledge gaps in the field of HIV and chronic pain and identify measurement domains in studies of chronic pain in PWH. Responses were ranked in order of importance in Questionnaire 2, which was followed by a group discussion. 29 experts completed Questionnaire 1, 25 completed Questionnaire 2, and 21 participated in the group. Many important clinical and research priorities emerged, including the need to examine etiologies of chronic pain in PWH. Pain-related measurement domains were discussed, with a primary focus on domains that could be assessed in a standardized manner across various cohorts that include PWH in different countries. We collaboratively identified clinical and research priorities, as well as gaps in standardization of measurement domains, that can be used to move the field forward.
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 | 0.141 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".