MR evidence of long T<sub>2</sub> water in pathological white matter
Bibliographic record
Abstract
PURPOSE: To describe what, if any, specific long T(2)-related abnormalities occur in the white matter of subjects with either phenylketonuria (PKU) or multiple sclerosis (MS). MATERIALS AND METHODS: The 48-echo T(2) relaxation data (maximum TE = 1.12 sec) were acquired from 15 PKU subjects, 20 MS subjects, and 15 healthy volunteers. Regions of interest were drawn in diffuse white matter hyperintensities (DiffWM), lesions, normal-appearing white matter (NAWM), and normal white matter. Long T(2) maps (200 msec < T(2) < 800 msec) were created for each subject. RESULTS: A new water reservoir with a markedly prolonged T(2) peak was identified in DiffWM and NAWM in 12 out of 15 subjects with PKU and a long T(2) signal was also seen in 23/97 lesions in 50% of subjects with MS. Additionally, a long T(2) component was observed in the corticospinal tracts of 10 healthy volunteers. The characteristics of the long T(2) signal were unique for each subject group. Potential sources of this signal include vacuolation and increases in extracellular water. CONCLUSION: This study supports the usefulness of increasing the data acquisition window of the multiecho T(2) relaxation sequence to better characterize the T(2) decay from pathological brain.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".