L’absence épileptique de l’enfance dont le controle nécessite plus d’un medicament.
Bibliographic record
Abstract
INTRODUCTION: Absence epilepsy is the most common primary generalized epilepsy syndrome encountered in pediatric practice. Treatment is pharmacologically specific and usually successful with a single medication. The objective of this study was to identify any clinical or electroencephalographic features at initial presentation in a consecutive cohort of children with absence epilepsy that may be associated with the need for a second medication. METHODS: A computerized pediatric neurology database (1991-2007 inclusive) was retrospectively searched for all patients with typical absence seizures, 3 Hz spike and wave on EEG and no apparent symptomatic etiology who were over the age of two years at seizure onset with at least one year of follow-up. All such children were then divided into two groups; a) those requiring a single medication for seizure control (Group 1), and b) those requiring two medications for seizure control despite optimal management with the initial medication as determined by serum drug monitoring (Group 2). Clinical and electrographic features evident at diagnosis were then contrasted between Group 1 and 2. RESULTS: Seventy-five children with absence seizures were initially identified with 52 meeting the study's inclusion and none of the exclusion criteria. Of these 52 children, 43 required a single medication for seizure control (Group 1), while 9 required two or more medications for seizure control (Group 2). A significant difference (p < 0.05) was apparent between Group 1 and 2 with respect to gender (16/43 males vs 8/9 males) and mean age of diagnosis (8.19 years +/- 3.00 vs 6.06 years +/- 2.22). Age of onset of seizures, interval duration of seizures prior to treatment initiation, duration of seizures, presence of automatisms, family history, presence of co-morbid conditions and EEG findings were not found to be significantly different between the two Groups. CONCLUSIONS: Male gender and an earlier age of diagnosis is associated with the need for two medications for seizure control in children with absence epilepsy. This observation may suggest the need for more intensive early programmatic follow-up for young male children with newly diagnosed absence epilepsy to effect more rapid attainment of seizure control.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".