Native Joint Septic Arthritis: Epidemiology, Clinical Features, and Microbiological Causes in a New Zealand Population
Bibliographic record
Abstract
OBJECTIVE: To determine the epidemiology, clinical features, and microbiology of adult native joint septic arthritis in Canterbury, New Zealand, over a 5-year period in individuals with and without an underlying rheumatic disorder. METHODS: Patients with native joint septic arthritis were identified retrospectively and classified by Newman's criteria. The clinical characteristics were described and comparisons made between those with and without underlying rheumatic disease. RESULTS: Two hundred forty-eight cases of native joint septic arthritis (mean age 60, range 16-97 yrs) were identified with an overall incidence rate of 12.0/100,000/year (95% CI 10.6-13.6). Yearly incidence increased with age to a maximum of 73.4/100,000 in those > 90 years of age. Septic arthritis was iatrogenic in 16.9% of cases while 27% had an underlying inflammatory arthritis including gout (14.9%), calcium pyrophosphate disease (8.5%), and rheumatoid arthritis (4%). Few patients were taking immunosuppressant therapy, with just 1 taking a biological agent. Staphylococcus aureus was the most commonly identified organism. Those with underlying inflammatory arthritis were significantly older (73.6 yrs vs 55.6 yrs; p < 0.001), more likely to be female (55.2% vs 26.0%; p < 0.001), and to have septic polyarthritis (16.4% vs 4.4%; p = 0.002). The 30-day mortality was 2%, increasing to 6% at 90 days. CONCLUSION: The incidence of septic arthritis in Canterbury, New Zealand, is higher than in previous studies. Crystal arthropathy commonly coexisted with infection although autoimmune arthritis and immunosuppression was less of a factor than anticipated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".