A.2 Clinical application of T1-w/T2-w ratio images for in vivo comparisons of myelin content in patients with trigeminal neuralgia
Bibliographic record
Abstract
Background: Novel magnetic resonance (MR) imaging techniques prompted the emergence of T1-w/T2-w images or “myelin-sensitive maps (MMs)” to measure myelin in vivo. However, acquisition-related variations in MR intensities prevent meaningful quantitative comparisons between MMs. We propose an improved pipeline to standardize MMs that is applied to patients with classic trigeminal neuralgia (CTN) and trigeminal neuralgia secondary to multiple sclerosis (MSTN). Methods: 3T scanner was used to obtain T1-w and T2-w images for 17 CTN and 17 MSTN patients. Template images were obtained from ICBM152 database. MS plaques and normal-appearing white matter (NAWM) were labelled. A Gaussian curve-fit was applied to the histogram of the intensity distribution of each patient image, and transformed to match the Gaussian curve-fit of the template image. Results: MM intensities were decreased within MS plaques, compared to NAWM in MSTN patients (p<0.001) and its corresponding regions in CTN patients (p<0.001). Qualitatively, the standardized patient image and its histogram better resembled the ICBM152 template. Conclusions: MM analysis revealed reduced myelin content in MS plaques compared to corresponding regions in CTN patients and surrounding NAWM in MSTN patients. The standardized MM serves as a non-invasive, clinical tool for quantitative analyses of myelin content between different brain regions and different patients in vivo.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".