Rates of, and risk factors for, septic arthritis in patients with invasive pneumococcal disease: prospective cohort study
Bibliographic record
Abstract
BACKGROUND: There are many case reports of septic arthritis complicating invasive pneumococcal disease (IPD); however, no study has compared patients with IPD with septic arthritis to those who didn't develop septic arthritis Thus, we aimed to determine the rates of, and risk factors for, septic arthritis in patients with invasive pneumococcal disease (IPD). METHODS: Socio-demographic, clinical, and serological data were captured on all patients with IPD in Northern Alberta, Canada from 2000 to 2014. Septic arthritis was identified by attending physicians. Descriptive statistics and multivariate analyses were used to compare characteristics of those with septic arthritis and IPD to those who did not. RESULTS: Septic arthritis developed in 51 of 3251 (1.6%) of patients with IPD. Inability to walk independently, male sex, and underlying joint disease were risk factors for developing septic arthritis in patients with IPD. Capsular serotypes 22 and 12F were more common in patients with septic arthritis than those without. CONCLUSIONS: In patients with IPD, septic arthritis is uncommon. Certain risk factors such as walking with or without assistance and underlying joint disease make biological sense as damaged joints are more likely to be infected in the presence of bacteremia. TRIAL REGISTRATION: Not applicable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".