Bibliographic record
Abstract
OBJECTIVES/SPECIFIC AIMS: Determine whether GABA-A receptor binding is abnormal and linked to dystonia symptoms in cervical dystonia (CD). METHODS/STUDY POPULATION: There is increasing evidence that a key pathophysiological mechanism in adult-onset focal dystonia is a reduction in inhibitory control over the sensorimotor network. Results from a recent 11C-flumazenil PET imaging study suggest that abnormal inhibitory signaling in genetic and sporadic forms of dystonia may be due to reduced GABA-A binding. It remains unknown whether CD, the most common form of adult-onset focal dystonia, is associated with abnormal GABA-A binding. The goal of this research is to determine if GABA-A receptor binding is abnormal and linked to dystonia symptoms in CD. RESULTS/ANTICIPATED RESULTS: We investigated whole brain GABA-A binding in 15 CD patients (11F; 64±8 y) and 15 healthy controls (10F; 64±9 y) using 60-minute dynamic 11C-flumazenil PET scans. GABA-A receptor binding potential (BP) was estimated using a simplified reference tissue model. A 2-sample t-test was used to identify voxel-wise GABA-A BP differences between groups, and a regression analysis used to test for correlations between GABA-A BP and disease severity as measured with the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). A conventional region of interest analysis was also conducted to quantify BP changes within the sensorimotor network using the automated anatomical labeling atlas. DISCUSSION/SIGNIFICANCE OF IMPACT: CD patients have reduced GABA-A receptor binding compared with healthy controls, with the greatest reduction seen within the sensorimotor region of the thalamus. Furthermore, reductions in GABA-A binding in brain regions associated with coupling sensory and motor information predict motor severity. These findings support that reduced GABAergic signaling within sensorimotor integration regions is a key mechanism underlying dystonic symptoms in CD and could help inform the development of better, more targeted treatment options.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.718 | 0.614 |
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".