Polymerase chain reaction detection of Kingella kingae in children with culture-negative septic arthritis in eastern Ontario
Bibliographic record
Abstract
BACKGROUND: The bacterium Kingella kingae may be an under-recognized cause of septic arthritis in Canadian children because it is difficult to grow in culture and best detected using molecular methods. OBJECTIVES: To determine whether K kingae is present in culture-negative joint fluid specimens from children in eastern Ontario using polymerase chain reaction (PCR) detection methods. METHODS: K kingae PCR testing was performed using residual bacterial culture-negative joint fluid collected from 2010 to 2013 at a children's hospital in Ottawa, Ontario. The clinical features of children with infections caused by K kingae were compared with those of children with infections caused by the 'typical' septic arthritis bacteria, Staphylococcus aureus and Streptococcus pyogenes. RESULTS: A total of 50 joint fluid specimens were submitted over the study period. Ten were culture-positive, eight for S aureus and two for S pyogenes. Residual joint fluid was available for 27 of the 40 culture-negative specimens and K kingae was detected using PCR in seven (25.93%) of these samples. Children with K kingae were significantly younger (median age 1.7 versus 11.3 years; P=0.01) and had lower C-reactive protein levels (median 23.8 mg/L versus 117.6. mg/L; P=0.01) than those infected with other bacteria. CONCLUSIONS: K kingae was frequently detected using PCR in culture-negative joint fluid specimens from children in eastern Ontario. K kingae PCR testing of culture-negative joint samples in children appears to be warranted.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".