A Review of Infections in People Who Use Nonprescription Drugs
Bibliographic record
Abstract
La dépendance accroît le risque d’infection chez une personne par des facteurs directs et indirects. Le risque direct d’infection par injection ou inhalation de substances est bien reconnu. Cependant, les voies indirectes, telles que les effets immunosuppresseurs de certaines drogues et les conditions sociales de la dépendance, peuvent augmenter le risque d’infection. Environ 200 millions de personnes (5% de la population adulte mondiale) consomment des drogues illégales chaque année et au Canada, près de 5 millions de personnes en 2015. Pour ceux qui gèrent une dépendance, une approche personnalisée visant à réduire les risques d’infection, suivie d’un dépistage, d’un diagnostic précoce et d’un lien avec les programmes de traitement est essentielle. Pour ceux qui traitent une infection chez des personnes présentant un trouble lié à l’utilisation de substances, une approche holistique peut être nécessaire pour atteindre les objectifs du traitement au-delà des protocoles de gestion standard. Le travail d’équipe est généralement essentiel. Il implique: le patient, les services spécialisés en toxicomanie, le travail social, la pharmaceutique et les spécialistes des maladies infectieuses que tous communiquent ensemble afin d’optimiser les résultats. Dans cette analyse, nous visons à mettre en évidence les infections courantes et importantes sur le plan clinique reliées à la médecine de la toxicomanie, afin de contribuer à la prévention, à l’identification, au diagnostic et au traitement optimal de telles infections. Abstract Addiction heightens an individual's risk for infection through both direct and indirect factors. The direct risk of infection from injecting or inhaling substances is well recognized. Indirect pathways, however, such as immunosuppressive effects of some drugs and the social circumstances of addiction may further increase the risk of infection. Approximately 200 million people (5% of the global adult population) use illegal drugs in any given year, and in Canada, this included nearly 5 million people in 2015. For those managing addiction, a customized approach to reduce the risks for infection followed by screening, early diagnosis, and linkage to treatment programs is essential. For those treating infection in persons with substance use disorder, a holistic approach may be required to achieve treatment goals beyond standard management protocols. Teamwork is usually essential involving; the patient, addictions services, social work, pharmacy, and infectious disease specialists all communicating to optimize outcomes. In this review, we aim to highlight common and clinically important infections that interface with addiction medicine, in order to help prevent, identify, diagnose, and optimally treat such infections.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".