The AO trauma CPP bone infection registry: Epidemiology and outcomes of <i>Staphylococcus aureus</i> bone infection
Bibliographic record
Abstract
Bone infection represents a serious complication of orthopedic surgery and Staphylococcus aureus is the most common pathogen. To improve the understanding of host-pathogen interaction, we developed a biospecimen registry (AO Trauma CPP Bone Infection Registry) to collect clinical data, bacterial isolates, and serum from patients with S. aureus bone infection. A prospective multinational registry with a 12-month follow-up was created to include adult patients (18 years or older) with culture-confirmed S. aureus infection in long bones after fracture fixation or arthroplasty. Baseline patient attributes and details on infections and treatments were recorded. Blood and serum samples were obtained at baseline, 6, and 12 months. Patient-reported outcomes were collected at 1, 6, and 12 months. Clinical outcomes were recorded. Two hundred and ninety-two patients with fracture-related infection (n = 157, 53.8%), prosthetic joint infection (n = 86, 29.5%), and osteomyelitis (n = 49, 16.8%) were enrolled. Methicillin-resistant S. aureus was detected in 82 patients (28.4%), with the highest proportion found among patients from North American sites (n = 39, 48.8%) and the lowest from Central European sites (n = 18, 12.2%). Patient outcomes improved at 6 and 12 months in comparison to baseline. The SF-36 physical component summary mean (95% confidence interval) score, however, did not reach 50 at 12 months. The cure rate at the end of the study period was 62.1%. Although patients improved with treatment, less than two-thirds were cured in 1 year. At 12-month follow-up, patient-reported outcome scores were worse for patients with methicillin-resistant S. aureus infections.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".