Is temperature regulation different in children susceptible to febrile seizures?
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between the presence and magnitude of fever and susceptibility to febrile seizures, defined as a known family history of febrile seizures. METHODS: Reanalysis of a case-control study dataset (Am J Dis Child. 1993; 147: 35-39). The magnitude of presenting fever was examined between the incident febrile seizure group (N = 75) and febrile control group (N = 150) for a family history of febrile seizures. The presence of fever was examined between the febrile control group (N = 150) and the afebrile control group (N = 150) for a family history of febrile seizures. RESULTS: Children with incident febrile seizures had a higher temperature in the emergency department than febrile controls (39.3 degrees C vs 39.0 degrees C, p = .004). Febrile control children with a known family history of febrile seizures had higher temperatures than those without a known family history (39.5 degrees C vs 38.9 degrees C, p = .04). A model of fever magnitude within the febrile group (seizures and controls) suggested that most of this relationship was on the basis of family history of febrile seizures rather than seizure or control status, with a possibility of interaction. Within the control children (febrile and afebrile), a known family history of febrile seizures was associated with fever (OR 3.4, 95% CI: 1.1,10.7). CONCLUSIONS: Children susceptible to febrile seizures through a known family history of febrile seizures appear more likely to present to emergency departments with fever, and when compared to their febrile counterparts, a fever of higher magnitude. This data supports Rantala's assertion "It may be that regulation of temperature is different in children susceptible to febrile seizures".
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".