Clinical Decision Analysis in the Treatment of Acute Otitis Media in a Child Over 2 Years of Age
Bibliographic record
Abstract
Acute otitis media (AOM) is the most common infection diagnosed in children. In Canada and the United States, the standard of care for treatment of children over 2 years of age diagnosed with AOM is a course of antibiotics for 5 to 10 days. However, in other countries, treatment is primarily symptomatic, and antibiotics are prescribed only if symptoms fail to resolve. Clinical decision analysis is a process whereby different treatment options are assessed systematically. All clinical pathways are incorporated into a model, probabilities for each event are determined from the literature, and clinical outcomes are quantified as to the preference of patients. The decision analysis then determines the most appropriate treatment option for the disease process. For AOM in a child over 2 years of age, four treatment options were considered including observation followed by 10 days of antibiotic therapy if required for failure of symptoms to resolve, observation followed by 5 days of antibiotic therapy if required, 10 days of antibiotic therapy when the child was initially diagnosed with AOM, and 5 days of antibiotic therapy when the child was initially diagnosed with AOM. Using a clinical decision analysis model for the treatment of AOM in a child over 2 years of age, the most appropriate treatment was found to be initial observation followed by 5 days of an antibiotic if the child failed to improve spontaneously. The decision analysis model developed was designed to be free of construction bias and was found to be robust in multiple sensitivity analysis.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".