Which Characteristics of Children With a Febrile Seizure Are Associated With Subsequent Physician Visits?
Bibliographic record
Abstract
OBJECTIVE: To reanalyze an existing data set to determine which children with an initial febrile seizure have excessive subsequent physician visits. METHODS: Individual data from a regional cohort of 75 children with a first febrile seizure and 150 febrile and 150 afebrile control subjects were linked to a comprehensive physician services database. The impact of study variables on subsequent physician utilization over the following 6 years was modeled using analysis of variance. RESULTS: Children with a known family history of febrile seizures at the time of study entry had 24% fewer physician visits. Control children with a known family history of afebrile seizures had 7% fewer visits than those with negative family histories. Children with an initial febrile seizure had 45% more physician visits when they knew of a relative with afebrile seizures than those with negative family histories. CONCLUSIONS: Knowing the family history of seizures is probably a marker of reduced physician utilization. At the time of an initial febrile seizure, knowing the family history of afebrile seizures defines a group of patients with excessive subsequent physician visits.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".