Risk Factors Of Infective Endocarditis In Persons Who Inject Drugs
Bibliographic record
Abstract
Abstract Background: The rising incidence of infective endocarditis (IE) among people who inject drugs (PWID) has been a major concern across North America. Details of injection practices leading to IE are not well characterized.Methods: A case-control study, using one-on-one interviews to understand risk factors and injection practices associated with IE among PWID was conducted. Eligible participants included those who had injected drugs within the last 3 months, were > 18 years old and either never had or were currently admitted for an IE episode. Cases were recruited from the tertiary care centers and controls were recruited from outpatient clinics in patients without IE and addiction clinics in London, Ontario. Results: 33 cases (PWID IE+) and 102 controls (PWID but IE-) were interviewed. Using clean injection equipment from the provincial distribution network was a protective factor against IE (p<0.001). Furthermore, using lighters during the injection process was also protective for IE (OR 2.5; 95% CI 1.11–5.63). Female sex (OR 3.63; 95% CI 1.58-8.36) and injection into multiple sites (OR 4.31; 95% CI 1.33-13.93) were associated with IE. Injection into the feet (57.6% cases; 36.6% control; p= 0.034) was also associated with IE. Discussion: Our pilot study highlights the importance of distributing clean injection materials for IE prevention. Injection into multiple areas may indicate a greater difficulty in accessing common and safer injection sites such as the arm, and thus multi-site injections may be a surrogate marker for injection-related venous damage in entrenched drug users. Moreover, the use of lighters may be correlated with the best practice of heating preparations of drugs prior to injection, which is known to reduce bacterial burden. Lastly, gender differences in injection techniques, which may place women at higher risk of IE, requires further study.
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 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 | 0.002 |
| 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 | 0.001 | 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".