Invasive Bacterial Infections in Afebrile Infants Diagnosed With Acute Otitis Media
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of invasive bacterial infections (IBIs) and adverse events in afebrile infants with acute otitis media (AOM). METHODS: We conducted a 33-site cross-sectional study of afebrile infants ≤90 days of age with AOM seen in emergency departments from 2007 to 2017. Eligible infants were identified using emergency department diagnosis codes and confirmed by chart review. IBIs (bacteremia and meningitis) were determined by the growth of pathogenic bacteria in blood or cerebrospinal fluid (CSF) culture. Adverse events were defined as substantial complications resulting from or potentially associated with AOM. We used generalized linear mixed-effects models to identify factors associated with IBI diagnostic testing, controlling for site-level clustering effect. RESULTS: Of 5270 infants screened, 1637 met study criteria. None of the 278 (0%; 95% confidence interval [CI]: 0%–1.4%) infants with blood cultures had bacteremia; 0 of 102 (0%; 95% CI: 0%–3.6%) with CSF cultures had bacterial meningitis; 2 of 645 (0.3%; 95% CI: 0.1%–1.1%) infants with 30-day follow-up had adverse events, including lymphadenitis (1) and culture-negative sepsis (1). Diagnostic testing for IBI varied across sites and by age; overall, 278 (17.0%) had blood cultures, and 102 (6.2%) had CSF cultures obtained. Compared with infants 0 to 28 days old, older infants were less likely to have blood cultures (P < .001) or CSF cultures (P < .001) obtained. CONCLUSION: Afebrile infants with clinician-diagnosed AOM have a low prevalence of IBIs and adverse events; therefore, outpatient management without diagnostic testing may be reasonable.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".