Prevalence and correlates of neck injection among people who inject drugs in <scp>T</scp>ijuana, <scp>M</scp>exico
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Injecting drugs in the neck has been related to adverse health conditions such as jugular vein thrombosis, deep neck infections, aneurysm, haematomas, airway obstruction, vocal cord paralysis and wound botulism, among others. We identified prevalence and correlates of neck injection among people who inject drugs (PWID) in Tijuana, Mexico. DESIGN AND METHODS: Beginning in 2011, PWID aged ≥18 years who injected drugs within the last month were recruited into a prospective cohort. At baseline and semi-annually, PWID completed interviewer-administered surveys soliciting data on drug-injecting practices. Logistic regression was used to identify predictors of injecting in the neck as the most frequent injection site at a single visit. RESULTS: Of 380 PWID, 35.3% injected in the neck at least once in the past 6 months, among whom 71.6% reported it as their most common injection site, the most common injecting site after the arms (47%). Controlling for age, years injecting and injecting frequency, injecting heroin and methamphetamine two or more times per day and having sought injection assistance were associated with injecting in the neck [adjusted odds ratios (AOR): 2.12; 95% confidence intervals (CI): 1.27-3.53 and AOR: 2.65; 95% CI: 1.52-4.53 respectively]. DISCUSSION AND CONCLUSIONS: Injecting in the neck was very common among PWID in Tijuana and was associated with polydrug use and seeking injection assistance. Tailoring harm reduction education interventions for individuals who provide injection assistance ('hit doctors') may allow for the dissemination of safe injecting knowledge to reduce injection-related morbidity and mortality.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".