Bibliographic record
Abstract
Modeling the effects of clinical magnetization transfer (MT) scans, which generate contrast using short, shaped radiofrequency (RF) pulses (pulsed MT), is complex and time-consuming. As a result, several studies have proposed approximate methods for a simplified analysis of the experimental data. However, potential differences in the MT parameters estimated by each method may complicate the comparison of reported results. In this study we evaluated three approximate methods currently used in quantitative MT (qMT) studies. In the first part of the investigation, an MT modeling technique that makes minimal approximations, other than the use of a two-pool tissue representation, was developed and validated. Subsequently, this technique served as a standard against which to evaluate the other, more approximate models. Each model was used to fit experimental data from samples of wild-type (WT) and shiverer mouse spinal cord, as well as simulated data generated by our minimal approximation modeling technique. The results of this study demonstrate that the approximations used in pulsed MT modeling are quite robust. In particular, it was shown that the semisolid pool fraction, M(0)(B), which is known to correlate strongly with myelin content, and the transverse relaxation time of macromolecular protons, T(2)(B), could be evaluated with reasonable accuracy regardless of the model used.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".