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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it