Hospitalization for Acute Otitis Media as a Useful Marker for Disease Severity
Bibliographic record
Abstract
BACKGROUND: An increase in severe complications to otitis media is a potential threat to antibiotic restrictions and is difficult to measure due to its low-prevalent nature. Easily accessible indicators sensitive to illness change are needed to benchmark the judicious use of antibiotics. OBJECTIVE: To investigate whether there has been a constant increase of hospital admissions for acute otitis media after the year 2000. METHODS: Registry-based study with complete data on hospitalization for acute otitis media and acute mastoiditis in Norway during 1999 to 2006. RESULTS: Mean incidence rate for acute otitis media hospitalization was 22.4 per 10,000 children and peak incidence in the second year of life 52.2 per 10,000 children. Corresponding mean incidence rate and peak incidence for acute mastoiditis were 1.5 and 3.5 per 10,000 children in the second year of life, respectively. There was a gradient increase of the incidence rates of acute otitis media hospitalization from the year 2000 to 2006 considering the Poisson regression model with a significant test of linear trend. CONCLUSIONS: Hospital admission for acute otitis media is prevalent enough to be a useful marker for otitis media severity and its distribution proportionate to that of acute mastoiditis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".