Seasonal trends and risk factors in prosthetic joint infections: A retrospective analysis
Bibliographic record
Abstract
Purpose Prior studies on the seasonal influence have yielded mixed results, with European studies linking warmer seasons to increased PJI rates, while North American data are less conclusive. We tried to determine if different seasons effect the incidence of acute and chronic PJIs. In addition we aimed to investigate if there was a correlation between PJI and age, BMI , surgeon, operating times, operating rooms, Diabetes, RA, end stage renal disease , congestive heart failure , alcohol or drug abuse, Charlson comorbidity index , surgical assist and ASA score. Methods A single-center retrospective review was conducted on patients with PJIs at a tertiary center from April 2012 to May 2024. A total of 114 cases of PJI were analyzed and data collection included demographic, comorbidity, and surgical details such as season of surgery, body mass index (BMI), age, surgeon, assistant, operating room nurses, comorbidities, anesthesia type, and postoperative anticoagulation . Results Among 114 patients with PJIs, acute PJIs were more common in winter (28 %) and summer (26 %), though findings were not statistically significant (p = 0.596). Late PJIs had higher prevalence in winter and fall (31 %) (p = 0.596). THA patients were more likely to experience acute PJI, whereas late PJI was more common in TKA patients (p = 0.002). We did find a “somewhat strong” association between the individual surgeons and the occurrence of PJI's (Cramer's V = 0.498). The majority of patients in the acute PJI had an ASA score of≥2, while the majority of patients in the late PJI group had an ASA score of 2 (p = 0.031). Conclusion Acute prosthetic joint infections (PJI) were found to occur more frequently in winter and summer, while late PJIs occurred more often in fall and winter, though the differences were not statistically significant. Acute PJIs were more common in total hip arthroplasties (THAs) and associated with an ASA score ≥2, while late PJIs were primarily seen in total knee arthroplasties (TKAs) with an ASA score of 2. No correlation was identified between PJIs and factors such as BMI, age, operating conditions, or comorbidities like diabetes, COPD , or rheumatoid arthritis .
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".