Eslicarbazepine acetate response in intellectual disability population versus general population
Bibliographic record
Abstract
BACKGROUND: A quarter of people with intellectual disability (ID) have epilepsy, compared to approximately one in a hundred across the general population. Evidence for the safe and effective prescribing of antiepileptic drugs (AEDs) for those with ID is, however, limited. AIMS OF STUDY: This study seeks to strengthen the research evidence around Eslicarbazepine Acetate (ESL), a new AED, by comparing response of individuals with ID to those from the general population who do not have ID. METHODS: A single data set was created through retrospective data collection from English and Welsh NHS Trusts. The UK-based Epilepsy Database Research Register (Ep-ID) data collection and analysis method were used. RESULTS: Data were collected for 93 people (36 ID and 57 'no ID'). Seizure improvement of '>50%' was higher at 12 months for 'no ID' participants (56%), compared to ID participants (35%). Retention rates were slightly higher for those with ID (56% compared to 53%). Neither difference was significant. CONCLUSIONS: Tolerance and Efficacy for ID and 'no ID' people in our data set were similar. Seizure improvement and retention rates were slightly lower than that found in other European data sets, but findings strengthen the evidence for the use of ESL in the ID population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".