A companion to the preclinical common data elements on neurobehavioral comorbidities of epilepsy: a report of the <scp>TASK</scp>3 behavior working group of the <scp>ILAE</scp>/<scp>AES</scp> Joint Translational Task Force
Bibliographic record
Abstract
The provided companion has been developed by the Behavioral Working Group of the Joint Translational Task Force of the International League Against Epilepsy (ILAE) and the American Epilepsy Society (AES) with the purpose of assisting the implementation of Preclinical Common Data Elements (CDE) for studying and for reporting neurobehavioral comorbidities in rodent models of epilepsy. Case Report Forms (CRFs) are provided, which should be completed on a per animal/per test basis, whereas the CDEs are a compiled list of the elements that should be reported. This companion is not designed as a list of recommendations, or guidelines for how the tests should be run-rather, it describes the different types of assessments, and highlights the importance of rigorous data collection and transparency in this regard. The tests are divided into 7 categories for examining behavioral dysfunction on the syndrome level: deficits in learning and memory; depression; anxiety; autism; attention deficit/hyperactivity disorder; psychosis; and aggression. Correspondence and integration of these categories into the National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) is introduced. Developmental aspects are addressed through the introduction of developmental milestones. Discussion includes complexities, limitations, and biases associated with neurobehavioral testing, especially when performed in animals with epilepsy, as well as the importance of rigorous data collection and of transparent reporting. This represents, to our knowledge, the first such resource dedicated to preclinical CDEs for behavioral testing of rodents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.025 |
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".