HIV Drug Resistance and HIV Transmission Risk Behaviors Among Active Injection Drug Users
Bibliographic record
Abstract
HIV(+) injection drug users in clinical care may harbor and transmit drug-resistant HIV. We performed a retrospective study of HIV drug resistance and risk behavior among HIV(+) injection drug users in care to determine the number of needle-sharing events that involved and the proportion of sharing partners exposed to drug-resistant HIV. Among 180 HIV injection drug users, 55 (31%) reported injecting drugs in the previous month, and 22 of these (40%) shared needles and/or works 148 times with 296 partners, of whom 271 (92%) were thought to be HIV(-) or status unknown. Further, 55 (31%) drug users harbored resistant HIV, including 5 (3% of total) who also shared needles and/or works a total of 27 times with 44 partners (18% of all sharing events and 15% of all exposed partners). A small proportion of injection drug users receiving clinical care engage in injection risk behavior and carry resistant HIV; however, because of multiple partners and needle-sharing events, they expose a substantial number of individuals to drug-resistant HIV. Strategies to reduce injection drug use risk behaviors among patients in clinical care are needed to reduce the transmission of sensitive and resistant HIV.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".