The Impact of Addictions Management Following Cardiac Surgery on People Who Inject Drugs and Have Infective Endocarditis
Bibliographic record
Abstract
Background Managing reinfection in patients who inject drugs and have undergone cardiac surgery could improve mortality. A significant gap in the management of addiction in this population exists and is rarely addressed during index hospitalization for surgical intervention. This study sought to determine if management of addiction changed rates of readmission for reinfection. Methods This study was a retrospective chart review and analysis. Patients who underwent cardiac surgery for IE due to injection drug use underwent a full chart review to determine if they received management of their addiction (Addictions Medicine Consultation, Social Work Consultation, Medication/Opioid Assisted Treatment (MAT/OAT), and Community Follow-Up) following their surgical intervention. Results A total of 41 patients were identified who fit the inclusion criteria. For addictions management – 43.2% of patients received an Addictions Consultation, 67.6% received a Social Work Consultation, 40.5% received MAT/OAT and 56.8% received Community Follow-Up. Overall mortality of these patients was 21.6% and 56.8% of patients were readmitted with reinfection. Multivariate logistic regression showed that patients who received intervention were 1.6 times more likely to be readmitted with reinfection (OR 1.65, 95% CI 0.29-9.41, p=0.5736). Females had a significantly higher odds of reinfection when adjusted for gender (OR 9.95, 95% CI 1.42-69.72, p=0.021). Conclusions We demonstrated a non-standardized approach to consultation and varying approaches to management of addiction. Patients who received intervention for addiction were more likely to be readmitted for reinfection - however, this was not significant. Future efforts include promoting formalized addictions consultation services for high-risk patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".