Health outcomes associated with illicit prescription opioid injection:A systematic review
Bibliographic record
Abstract
Prescription opioid injection is a growing concern among people who use illicit drugs. Little is known about the potential health-related harms of injecting prescription opioids. Therefore, the authors undertook a systematic review to identify health outcomes associated with injecting prescription opioids. PubMed, Ovid MEDLINE®, EMBASE, Journals@Ovid, CINAHL, PsycInfo, Web of Science® Core Collection, CAB Direct, and ERIC databases were searched to identify English articles published between January 1990 and February 2015 that matched the inclusion criteria. Potentially relevant articles were those examining a clinical health outcome among people who use illicit drugs, in which a sub-group injects prescription opioids. The International Classification of Diseases (ICD-10) was used to clinically classify health outcomes. In total, 31 studies that met the inclusion criteria were identified and summarized. A modified version of the Downs and Black checklist was used to assess individual study quality and identify sources of bias. Findings supported associations between prescription opioid injection and hepatitis C infection, substance dependence and other mental health indicators, and lower general health. Associations with human immunodeficiency virus, overdose, and cutaneous infection were less consistent and varied according to prescription opioid type(s). Several potential sources of bias were identified as well as a need for more longitudinal research and more rigorous confounding adjustment. The current findings highlight a need to consider the growing popularity of prescription opioid injection in efforts to reduce drug-related harm among people who inject drugs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".