Real-life experience with IV dalbavancin in Canada; results from the CLEAR (Canadian LEadership on Antimicrobial Real-life usage) registry
Bibliographic record
Abstract
OBJECTIVES: We report the use of IV dalbavancin in Canadian patients using data captured by the national CLEAR registry. METHODS: The CLEAR registry uses the web-based data management program, REDCap™ (online survey https://rcsurvey.radyfhs.umanitoba.ca/surveys/?s=TPMWJX98HL) to facilitate clinicians entering details associated with their clinical experiences using IV dalbavancin. RESULTS: Data were available for 40 patients. The most common infections treated were acute bacterial skin and skin structure infection (ABSSSI) (62.5% of patients), bone/joint infection (22.5%), bloodstream/vascular infection (7.5%) and endocarditis (5.0%). Dalbavancin was used as directed (75.0%) and empiric therapy (25.0%). MRSA was the most common identified pathogen (70.0%). Dalbavancin was used both in outpatient (e.g., emergency department) (65.0%), and inpatient treatment settings (e.g., hospital ward) (35.0%). Dalbavancin was used due to the convenience of a single dose treatment (77.5%) as well as to facilitate hospital discharge (7.5%). Dalbavancin was primarily used alone (90.0%), and most commonly using a single 1500 mg dose (77.5%). Microbiological success (pathogen eradicated or presumed eradicated) occurred in 88.2% of known cases, while clinical success (cure and/or improvement) occurred in 93.3% of known cases. No adverse events were reported. CONCLUSIONS: In Canada, IV dalbavancin is used as both directed and empiric therapy to treat ABSSSI as well as off-label (bone/joint, bacteremia/vascular, endocarditis, device-related) infections. It is used in both outpatient and inpatient settings due primarily to its convenience as a single-dose treatment regimen and to facilitate early hospital discharge. Dalbavancin use is associated with high microbiological and clinical cure rates along with an excellent safety profile.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".