Causative Pathogens, Antibiotic Susceptibility, and Characteristics of Patients with Bacterial Septic Arthritis over Time
Bibliographic record
Abstract
Septic arthritis is an important concern for rheumatologists in the evaluation of joint disease. The incidence of these infections is estimated at 4–10 cases per 100,000 patient-years, and the mortality rate ranges between 4% and 50%1,2. Management of septic arthritis remains a challenge because inappropriate treatment can lead to irreversible joint destruction. Over the past decade, the prevalence of antimicrobial-resistant pathogens, including methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant (MDR) gram-negative bacilli (GNB), has increased in the United States. More recently, S. aureus with reduced susceptibility to vancomycin has been reported3,4. There are only limited data that describe the incidence and outcomes of patients with septic arthritis caused by S. aureus with reduced susceptibility to vancomycin or MDR GNB. The objective of our study was to investigate the trends of causative pathogens, antibiotic susceptibility, and characteristics of patients with bacterial septic arthritis in the era of multidrug resistance. The institutional review board of Boston Medical Center approved this analysis (H-34459). A retrospective chart … Address correspondence to Dr. S. Jinno, Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA. E-mail: sadaoj{at}gmail.com
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".