Beina- og liðasýkingar barna á Íslandi af völdum baktería á tímabilinu 1996-2005
Bibliographic record
Abstract
OBJECTIVE: The main objective was to determine the incidence and causative pathogens of osteomyelitis and septic arthritis in Icelandic children, as well as presenting symptoms and diagnosis. METHODS: A nationwide retrospective review was done of all children <18 year old, 1996-2005. Subjects were divided into three equal age groups, 0-5, 6-11 and 12-17 years old. Cultures were reviewed and postive and negative cases compared. RESULTS: Over the study period 220 cases were identified, 161 osteomyelitis and 59 septic arthritis cases. The incidence increased significantly over the period (p=0.019), mostly in the youngest age group (p<0.001) with osteomyelitis. Incidence of cases with a pathogen identified was unchanged over the period while culture negative cases increased significantly (p<0.001). Median age for osteomyelitis (6,1 years) was higher than in cases of septic arthitis (1,8 years) (p=0.003). A pathogen was identified in 59% of cases with osteomyelitis and 44% with septic arthritis. S. aureus was most common (65% and 27%, respectively) and K. kingae was second most common pathogen (7% and 11%, respectively). Methicillin resistant S. aureus was not identified. The tibia and knee were the predominant sites for osteomyelitis and septic arthritis respectively. CONCLUSIONS: An increased incidence was found in the youngest age group with osteomyelitis, especially in cases without a pathogen identified. The most commonly cultured pathogen was S. aureus, followed by K. kingae. A more sensitive technique to identify pathogens might be indicated in culture negative cases.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".