To the Editor, Common Misconceptions Regarding Neuroimaging in Epilepsy Diagnosis
Bibliographic record
Abstract
I read with interest the excellent review of Saposnik et al 1 on movements in brain death.As the Authors stated, for the most part, these movements are considered to be spinal reflexes.Spinal reflex movements and automatisms occurring after brain death have been considered phylogenetically "old motor patterns," which may be set free when the cord is uncoupled from the "younger" input of the brainstem and neocortex 2 .Regarding the pathogenesis of these motor patterns, I suggest to introduce the term central pattern generator (CPG), thus referring to a concept widely present in the literature.Central pattern generators are genetically determined specialized neuronal networks localized in the brainstem and spinal cord, representing the anatomical substrate of stereotyped inborn fixed motor behaviours which are essential for survival.In humans CPGs are largely under neocortical control.Stereotyped action patterns, expression of genetically determined CPGs, have been described to occur in physiological movements in foetuses and newborns, in physiological sleep, in parasomnias and some epileptic seizures 3 .A cortical inactivation is currently considered underlie the pathophysiology of some of these motor patterns in both seizures and syncope 4 .It is rather surprising that although widely present in the literature, the term CPG never appears in relationship with brain death-associated reflexes and automatisms, which are nevertheless generated by spinal CPGs.I think that introducing the term CPGs also in the pathogenesis of death-associated motor activity would be conceptually useful, since it would provide a more global vision on neural networks generating motor behaviours occurring in several conditions, building therefore a bridge between life and death.
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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 | 0.003 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".