MRI for targeting in surgical treatment of movement disorders
Bibliographic record
Abstract
This chapter provides an overview of magnetic resonance imaging (MRI) methods. The focus on iron in Parkinson's disease (PD) imaging has remained an important topic and researchers have often utilized T2*, or its reciprocal R2*, in nigral imaging protocols. Some iron-sensitive methods have been recently developed. These include adiabatic T2ρ, magnetization transfer (MT) imaging, and susceptibility-weighted imaging (SWI). The authors have developed a novel rotating frame relaxation experiment called relaxation along a fictitious field (RAFF). There has been greater refinement with the utilization of methods that do not employ a-priori regions of interest (ROIs). One such method is voxel-based morphometry (VBM), in which there is standardization of data and then voxel-by-voxel comparison between group data to determine if there are differences in signal intensity. Diffusion tensor imaging (DTI) provides structural data based on the directionally restrained diffusion of water within fiber tracts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.042 |
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".