Recommendations for Preclinical Testing of Treatments Against Alzheimer’s Disease-Related Epileptiform Spikes in Transgenic Rodent Models
Bibliographic record
Abstract
Recent evidence suggests that about 30%of patients with mild to moderate Alzheimer's disease (AD) without a known diagnosis of epilepsy may display epileptiform spikes during electroencephalographic (EEG) recordings. These abnormal discharges occur predominantly during sleep and may be associated with accelerated disease progression. Subclinical spikes may represent a relevant target for clinical drug interventions, and there is a clear unmet need for preclinical testing of novel disease modifying agents in suitable animal models. Transgenic rodent models of AD pathology exhibit various forms of epileptiform EEG activity related to the abnormal levels of amyloid species in the brain. Among them, large-amplitude cortical and hippocampal EEG spikes in mouse and rat AD models may be reminiscent of the subclinical epileptiform EEG spikes recorded in some AD patients. This article reports the recommendations of a multidisciplinary panel of experts on optimal EEG markers and experimental designs to measure and report epileptiform activities and their response to symptomatic and disease-modifying drugs in transgenic AD model rodents. These recommendations may harmonize future preclinical EEG studies in the drug discovery research and may increase the comparability of experimental outcomes and their translational clinical value.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".