Optimising detection and prevention of prosthetic joint infections
Bibliographic record
Abstract
Translation of best practices to clinical practice can be a considerably lengthy process. Reducing surgical site infections (SSIs) following primary hip and knee arthroplasties is a crucial endeavour in light of the continued rise in the number of these operations being performed and the morbidity associated with prosthetic joint infections (PJIs).1 A number of interventions have been successful in lowering SSI rates following orthopaedic procedures, with those targeting Staphylococcus aureus particularly effective given that it is the most common pathogen.2 Measures to reduce SSIs are evidence-based, relatively straightforward and cheap, yet widespread implementation remains elusive. Perioperative staphylococcal decolonisation represents a substantial cost savings opportunity given the economic burden associated with PJIs, including revision operations, rehospitalisation and prolonged antibiotic courses .3 Calderwood et al 4 report on the impact of disseminating a SSI prevention bundle for hip and knee PJIs using a pre-existing platform designed for quality improvement initiatives. States with hospitals participating in the quality improvement initiative were compared with those who were interested in participating, revealing a reduction in SSI incidence following primary hip and knee arthroplasty. The size and scope of the intervention (193 hospitals in 5 states) were equally as impressive as the reduction in SSI rates observed (PJI reductions in intervention states exceeded the declines in comparator states by 12%–15%). The bundle included a number of simple measures: S. …
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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 | 0.007 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".