Oritavancin for Treatment of Osteomyelitis: A Systematic Review and Meta‐Analysis of Observational Studies
Bibliographic record
Abstract
ABSTRACT Background Osteomyelitis (OM) is a complex inflammatory bone infection typically requiring prolonged antibiotic therapy. Oritavancin (ORI), a long‐acting lipoglycopeptide with activity against biofilm‐embedded pathogens, has emerged as a potential treatment option despite previous US Food and Drug Administration (FDA) warnings against its use in OM. Methods We conducted a systematic review and meta‐analysis of observational studies in seven databases through August 8, 2024 (PROSPERO registration: CRD42025635473) to evaluate the available evidence on ORI's efficacy and safety in OM. Clinical success was defined as the improvement or resolution of infection without requiring additional gram‐positive antibiotics, surgical debridement, or amputation. Study quality was assessed using the Newcastle–Ottawa Scale. Results Our systematic review included nine observational studies comprising 316 patients with OM treated with ORI. Quality assessment using the Newcastle‐Ottawa Scale revealed scores ranging from 5/9 to 8/9, with most studies demonstrating adequate outcome assessment but limitations in cohort selection and comparability. Meta‐analysis demonstrated a pooled clinical success rate of 81% (95% CI: 76%–85%). Comparative analysis of two studies yielded an odds ratio of 2.99 (95% CI: 0.86–10.36) favoring ORI over comparators (daptomycin and dalbavancin), though with substantial heterogeneity ( I 2 = 80.2%, p = 0.0247). Conclusions Despite previous warnings, we found no evidence of ineffectiveness with ORI for OM. ORI's infrequent dosing schedule may provide convenience over daily parenteral therapy, particularly for difficult‐to‐treat pathogens like MRSA and VRE. Further research is needed to optimize dosing strategies based on pathogen susceptibility and establish appropriate therapeutic drug monitoring protocols.
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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".