Does screening for adverse effects improve health outcomes in epilepsy?
Bibliographic record
Abstract
Objective To determine whether systematic screening for adverse effects of antiepileptic drugs (AEDs) reduces toxicity burden and improves health-related quality of life in patients with epilepsy. Methods Consecutive patients with uncontrolled seizures aged ≥16 years and a high Adverse Event Profile (AEP) score were randomized to 2 groups and followed up for 18 months at 11 referral centers. AEP scores were made available to treating physicians at all visits in the intervention group, but not in the control group. Co–primary endpoints were changes in AEP scores and Quality of Life Inventory for Epilepsy-31 (QOLIE–31) scores. Results Of 809 enrolled patients able to complete the AEP questionnaire, 222 had AEP scores ≥45 and were randomized to the intervention (n = 111) or control group (n = 111). A total of 206 patients completed the 18-month follow-up. Compared with baseline, AEP scores decreased on average by 7.2% at 6 months, 12.1% at 12 months, and 13.8% at 18 months in the intervention group (p < 0.0001), and by 7.7% at 6 months, 9.2% at 12 months, and 12.0% at 18 months in controls (p < 0.0001). QOLIE-31 scores also improved from baseline to final visit, with a mean 20.7% increase in the intervention group and a mean 24.9% increase in the control group (p < 0.0001). However, there were no statistically significant differences in outcomes between groups for the 2 co–primary variables. Conclusions Contrary to findings from a previous study, systematic screening for adverse effects of AEDs using AEP scores did not lead to a reduced burden of toxicity over usual physician treatment. Italian Medicines Agency (AIFA) identifier FARM52K2WM_003. Clinicaltrials.gov identifier NCT03939507 (registered retrospectively in 2019; the study was conducted during the 2006–2009 period and registration of clinical trials was not a widely established practice when this study was initiated). Classification of evidence This study provides Class II evidence that the additional collection of formal questionnaires regarding adverse effects of AEDs does not reduce toxicity burden over usual physician treatment.
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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 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".