Surveillance of injecting‐related injury and diseases in people who inject drugs attending a targeted primary health care facility in Sydney's Kings Cross
Bibliographic record
Abstract
OBJECTIVE: This study examined the prevalence of injecting-related injuries and diseases (IRIDs) and associated risk factors among people who inject drugs (PWID) attending a primary health care facility in Sydney's Kings Cross. METHODS: We calculated prevalence of a wide range of IRIDs utilising data reported by 702 PWID who completed a clinician-administered survey at their first visit. Multivariable logistic regressions identified factors independently associated with at least one episode of: i) cutaneous and ii) non-cutaneous IRIDs. RESULTS: Lifetime prevalence of cutaneous IRIDs was 23%. Forty-two per cent of PWID with a history of abscess attended hospital at their most recent episode. Female gender, lifetime receptive syringe sharing (RSS), injecting while in custody, and ever injecting in places other than the arm were independently associated with reporting at least one episode of cutaneous IRIDs. Ever injecting in sites other than the arm, injecting for five or more years and lifetime history of RSS were independently associated with at least one episode of non-cutaneous IRIDs. CONCLUSIONS: IRIDs are a substantial health issue for PWID. Their ongoing surveillance is warranted particularly in primary care settings targeting PWID to inform prevention and early management, thus reducing complications that may require hospital admission.
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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 | 0.001 | 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 | 0.001 |
| 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".