The Burden of Surgical Site Infections With Pathogens Presumably Resistant to Perioperative Prophylaxis in Orthopedic Tumor Surgery: Secondary Analysis of the Prophylactic Antibiotic Regimens in Tumor Surgery (PARITY) Trial
Bibliographic record
Abstract
BACKGROUND: Surgical procedures for malignant bone tumors of the lower extremity are associated with a significant risk of surgical site infection (SSI). Little is known about the microbiology and risk factors for resistant SSIs in this population. METHODS: We describe the microbiological and other characteristics and of SSIs, as well as risk factors for antimicrobial resistance against antibiotics used for perioperative prophylaxis in a secondary analysis of the Prophylactic Antibiotic Regimens in Tumor Surgery (PARITY) trial population. The PARITY trial assessed the effects of short-term (24 hour) versus long-term (5-day) postoperative antibiotic prophylaxis on the SSI incidence in orthopedic oncology. RESULTS: SSIs were identified in 96 of 604 patients (15.9%), with ≥1 pathogen isolated in 73 (76.0%). The most common pathogens were coagulase-negative staphylococci (34.4%), Staphylococcus aureus (24.0%), and Enterobacterales (22.9%). The proportions of pathogens with presumed resistance against cephalosporins were similar in the 2 groups (65.9% in the short-term vs 71.9% in the long-term arm; odds ratio [OR], 0.76 [95% confidence interval, .28-2.06]; P = .58). Neutropenia (22.9% vs 4.8%; OR, 5.95 [95% confidence interval, .72-49.45]; P = .06) and initiation of antibiotics >7 days before SSI diagnosis (50.0% vs 34.8%; OR, 1.88 [.68-5.21; P = .22) were numerically but not statistically significantly more common in those with presumed resistance. CONCLUSIONS: SSIs due to pathogens presumably resistant to the systemic or local prophylactic agents used are common in patients undergoing reconstruction for bone tumors. The selection of presumably resistant pathogens is not driven by the duration of antibiotic prophylaxis; however, antibiotic-loaded cement was associated with resistance.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".