Treatment of Febrile Seizures: The Influence of Treatment Efficacy and Side-Effect Profile on Value to Parents
Bibliographic record
Abstract
OBJECTIVES: We examined parents' perception of the value of treatments designed to reduce the risk of febrile seizure recurrence. STUDY DESIGN: The families of 42 children with febrile seizures were recruited after pediatric or neuropediatric consultation. A mail questionnaire addressed the family's willingness to pay for a hypothetical treatment for febrile seizures with risk reductions for future febrile seizures of 25%, 50%, 75%, and 100%. The hypothetical clinical scenario was then modified to include the side- effect profiles of either daily phenobarbital or valproic acid, or intermittent diazepam prophylaxis. Covariates included the nature of the child's febrile seizure(s), parents' familiarity with febrile seizures, experiences at the time of febrile seizures or with medication side effects, education and income, and mastery and trait anxiety. RESULTS: Thirty-eight parents, representing 22 of 42 families, completed questionnaires. There was a dramatic inflection in parents' willingness to pay for 100% risk reduction as opposed to 75% or lower risk reductions. Introduction of side effects dramatically reduced the value attached to each level of treatment benefit. Nevertheless, a few parents (3/38) would pay "as much as it takes" to be rid of their child's recurrence risk. CONCLUSIONS: Given the range of value assigned to prophylactic medication for febrile seizures, management strategies for children with febrile seizures must be responsive to the needs and values of individual families.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".