Patient Characteristics and Outcomes of Outpatient Parenteral Antimicrobial Therapy: A Retrospective Study
Bibliographic record
Abstract
Outpatient parenteral antimicrobial therapy (OPAT) is a safe and effective alternative to hospitalization for many patients with infectious diseases. The objective of this study was to describe the OPAT experience at a Canadian tertiary academic centre in the absence of a formal OPAT program. This was achieved through a retrospective chart review of OPAT patients discharged from Sunnybrook Health Sciences Centre within a one-year period. Between June 2012 and May 2013, 104 patients (median age 63 years) were discharged home with parenteral antimicrobials. The most commonly treated syndromes included surgical site infections (33%), osteoarticular infections (28%), and bacteremia (21%). The most frequently prescribed antimicrobials were ceftriaxone (21%) and cefazolin (20%). Only 56% of the patients received follow-up care from an infectious diseases specialist. In the 60 days following discharge, 43% of the patients returned to the emergency department, while 26% required readmission. Forty-eight percent of the return visits were due to infection relapse or treatment failure, and 23% could be attributed to OPAT-related complications. These results suggest that many OPAT patients have unplanned health care encounters because of issues related to their infection or treatment, and the creation of a formal OPAT clinic may help improve outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".