NONINVASIVE EVALUATION OF PROGRESSION IN MS PATIENTS
Bibliographic record
Abstract
Introduction: Multiple sclerosis (MS) is the leading cause of disability in young adults. We aimed to monitor disease progression characteristics using cognitive and physical parameters with optical coherence tomography (OCT) and Magnetic Resonance Spectroscopy (MRS). Methods: Fifteen relapsing remitting (RRMS), thirteen secondary progressive (SPMS) and twelve primary progressive (PPMS) patients were included. The Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test (9 HPT), Timed 25-Foot Walk Test (T25FWT), Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS), Montreal Cognitive Assessment (MoCA), MRS and OCT examinations were performed at baseline and follow-up. Results: EDSS, Beck Depression Scale scores, 9-HPT and T25FWT duration scores were higher in the PPMS group compared to the other groups, whereas MoCA, SDMT, CVLT2, and BVMT-R scores were the lowest in this group. Retinal nerve fiber layer (RNFL) measurements in the right (p=0.023) and left (p=0.028) nasal quadrants were found to be higher in the RRMS group compared to the progressive groups. Baseline MRS showed a lower Thalamus myoinositol/creatinine (mI/Cr) ratio in progressed patients compared to stable patients (p=0.003). A cut-off value of baseline Thalamus mI/Cr ratio <0.066 for predicting disease progression based on baseline Thalamus mI/Cr was determined to be <0.066, with an 81.82% sensitivity, and 79.17% specificity, 64.29% positive predictive value (PPV), and 90.48% negative predictive value (NPV) (p=0.003). Conclusion: Early detection of disease progression has critical importance for MS. Besides prognostic serum or cerebrospinal fluid biomarker tests, noninvasive methods such as disability scales and/or imaging techniques may have a significant impact and are easily replicable. As an advanced imaging technique, MRS has the potential for ongoing tissue inflammation. In parallel with that, we have obtained a cut-off thalamic mI/Cr ratio value as a significant predictor of disease progression in MS patients.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".