How is ceftobiprole used in Canada: the CLEAR study final results
Bibliographic record
Abstract
BACKGROUND: We report the final results of the clinical usage of ceftobiprole in patients in Canada from data in the national CLEAR (Canadian Le adership on Antimicrobial Real-Life Usage) registry. RESEARCH DESIGN AND METHODS: The authors review the final data using the national ethics approved CLEAR study. Thereafter, the literature is surveyed regarding the usage of ceftobiprole to treat patients with infectious diseases via PubMed (up to March 2024). RESULTS: In Canada, ceftobiprole is primarily used as directed therapy to treat a variety of severe infections caused by MRSA. It is primarily used in patients failing previous antimicrobials, is frequently added to daptomycin and/or vancomycin with high microbiological and clinical cure rates, along with an excellent safety profile. Several reports attest to the microbiological/clinical efficacy and safety of ceftobiprole. Ceftobiprole is also reported to be used empirically in select patients with community-acquired bacterial pneumonia (CABP), as well as hospital-acquired bacterial pneumonia (HABP). CONCLUSIONS: In Canada, ceftobiprole is used mostly as directed therapy to treat a variety of severe infections caused by MRSA, in patients failing previous antimicrobials. It is frequently added to, and thus used in combination with daptomycin and/or vancomycin with high microbiological/clinical cure rates, and an excellent safety profile.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".