Human T-lymphotropic virus 1 and 2 among people who used illicit drugs in the state of Pará, northern Brazil
Bibliographic record
Abstract
People who used illicit drugs (PWUDs) represent an important population group for acquisition and viral dispersion. In Brazil, high rates of the human T lymphotropic virus 1 (HTLV-1) and 2 (HTLV-2) have been reported in epidemiological studies. However, the epidemiological scenario of HTLV-1/2 infections in PWUDs is still poorly understood. Thus, this cross-sectional study determined the prevalence, frequency of subtypes and factors associated with HTLV-1/2 infections among PWUDs in the Brazilian state of Pará, an area considered endemic for this virus and with poor health services. Among 826 PWUDs, 53 (6.4%) presented anti-HTLV-1/2 antibodies by EIA and 44 (5.3%) presented proviral DNA by PCR. HTLV-1 and HTLV-2 were detected in 25 (3.0%) and 19 (2.3%) PWUDs, respectively. Subtypes 1a (25/44), transcontinental (23/44) and Japanese subgroups (2/44), 2b (6/44) and 2c (13/44) were identified. Involvement in illicit/criminal activity, daily use of illicit drugs, illicit drug use over 12 years, unprotected sex with other PWUDs, changes in genitalia (including ulcers and wounds), and more than 12 sexual partners were associated with HTLV-1/2 in PWUDs. This high prevalence and intense circulation of subtypes and subgroups of HTLV-1/2 is very worrying, and indicate the need for urgent actions for its control, prevention and treatment.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".