Association between oropharyngeal carriage of <i>Kingella kingae</i> and osteoarticular infection in young children: a case–control study
Bibliographic record
Abstract
BACKGROUND: Kingella kingae has been increasingly identified in patients with osteoarticular infections. Our main objective was to evaluate the association between carriage of K. kingae in the oropharynx of preschool children and osteoarticular infections. METHODS: We conducted this prospective case–control study in 2 tertiary care pediatric hospitals (Canada and Switzerland) between 2014 and 2016. Potential cases were children aged 6 to 48 months with a presumptive diagnosis of osteoarticular infection according to the treating emergency physician. Confirmed cases were those with diagnosis of osteomyelitis or septic arthritis proven by positive findings on technetium-labelled bone scan or magnetic resonance imaging or identification of a microorganism in joint aspirate or blood. For each case, we recruited 4 age-matched controls from among children presenting to the same emergency department for trauma. The independent variable was presence of oropharyngeal K. kingae DNA identified by a specific polymerase chain reaction assay. We determined the association between oropharyngeal carriage of K. kingae and definitive osteoarticular infection. RESULTS: The parents of 77 children admitted for suspected osteoarticular infection and 286 controls were invited to participate and provided informed consent. We identified K. kingae in the oropharynx of 46 (71%) of 65 confirmed cases and 17 (6%) of 286 controls; these results yielded an odds ratio of 38.3 (95% confidence interval 18.5–79.1). INTERPRETATION: Detection of oropharyngeal K. kingae was strongly associated with osteoarticular infection among children presenting with symptoms suggestive of such infection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".