A Comparison Between Pharmacological Treatment Of Epileptic Patients With And Without Intellectual Disability. (P3.255)
Bibliographic record
Abstract
OBJECTIVE: In this study we compare anti-epileptic drug (AED) treatment of intellectually disabled (ID) patients and those with normal intellect (NI). DESIGN/METHODS: We reviewed the medical records of 100 epilepsy patients (50 ID and 50 NI and comparatively severe epilepsy), and recorded all current and past AEDs prescribed for epilepsy. RESULTS: Patients with ID were currently taking a greater number of AEDs (p=0.0001) and had been exposed to more AEDs in the past (p=0.005). There were no significant differences between the two groups in terms of past or present exposure to the newer AEDs as a group (lamotrigine, topiramate, levetiracetam, gabapentin, felbamate and tiagabine). Patients with ID were more likely to be currently taking an old AED (phenobarbital, primidone, phenytoin, carbamazepine or valproic acid) (p=0.01). More ID patients were currently taking (p=0.002) and had previously taken (p=0.004) a benzodiazepine (BZD) AED such as clonazepam, nitrazepam and clobazam. CONCLUSIONS: Patients with ID were currently taking a greater number of AEDs (p=0.0001) and had been exposed to more AEDs in the past (p=0.005). There were no significant differences between the two groups in terms of past or present exposure to the newer AEDs as a group (lamotrigine, topiramate, levetiracetam, gabapentin, felbamate and tiagabine). Patients with ID were more likely to be currently taking an old AED (phenobarbital, primidone, phenytoin, carbamazepine or valproic acid) (p=0.01). More ID patients were currently taking (p=0.002) and had previously taken (p=0.004) a benzodiazepine (BZD) AED such as clonazepam, nitrazepam and clobazam.
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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 | 0.001 |
| 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) | 0.003 | 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".