Retrospective Analysis of Septic Arthritis Caused by Intra-Articular Viscosupplementation and Steroid Injections in a Single Outpatient Center
Bibliographic record
Abstract
Background: Septic arthritis is defined by the presence of pathogen(s) in a joint by direct inoculation or hematogenous spread. Most common organisms include Staphylococcus aureus and Escherichia coli . Clinical presentation is fever, warmth and night pain, with most common joints involved being the knee and hip. Iatrogenic septic arthritis is an uncommon complication of intra-articular injection for osteoarthritis yet its complications can be devastating. We aim to highlight ten cases of iatrogenic septic arthritis in retrospective study reviewing symptoms, signs, laboratory data, causing organisms and reasons leading to those infections. Methods: Retrospective analysis of charts of ten patients who were admitted to Jersey Shore University Medical Center with diagnosis of iatrogenic septic arthritis. Results: Average age of patients is 69.9 years. Most common comorbidities seen in our patient were hypertension and diabetes mellitus. The most common intra-articular agents that were injected were cortisone and Synvisc. The mean incubation period was 11.9 days. Most common presenting symptoms were joint pain and swelling. The most common organism isolated in cultures was Streptococcus mitis . A total of 100% of patients underwent surgical intervention for septic arthritis. One case was complicated by sepsis. Conclusions: Iatrogenic septic arthritis is not common; however its complications can be catastrophic to patients. Improper sterile techniques and untrained physicians are the main risks factors for this complication. Physicians should take proper sterile measures to avoid complications of intra-articular injections. J Clin Med Res. 2019;11(7):480-483 doi: https://doi.org/10.14740/jocmr3838
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".