Bibliographic record
Abstract
WITH THE INCREASINGLY WIDESPREAD use of magnetization transfer (MT) measurements in clinical MR, it is important to be able to communicate effectively and quantitatively about MT ratios (MTRs) among different sites and over a wide diversity of MR imagers and pulse sequences. Berry and colleagues (1) are to be commended on their successful amassing of MTR data from six different European centers, using 16 different pulse sequences implemented on three different types of hardware. To be brought to successful fruition, such an endeavor requires excellent planning and coordination. Having gone to all their effort to measure comparative MTRs in white matter, it is surprising and disappointing to see the data treated essentially as biological scatter. Their strategy of plotting MTR versus average rotation angle per unit time (Fig. 2 in ref. 1) or versus B1 saturating field (Fig. 3 in ref. 1) and then performing fits to the data with several functions of arbitrary shape amounts to a kind of “correlational empiricism.” Such a groping approach is sometimes necessary if little is known about the underlying science. In the case of MT, however, precise and quantitative models exist for predicting MTRs based on fundamental biophysical properties of tissue and the specific details of the MR pulse sequences. For idealized continuous wave experiments, a wide range of MTRs from different sequence parameters can be accounted for with only 1%–2% residual error using a simple two-compartment representation of tissue (2). In fact, the quantification is so good that the model can be used to elucidate the nature of the absorption lineshapes of the macromolecular compartment in different tissues (3, 4). The same interpretive model can be applied to clinical pulsed MT sequences (5), for which has been shown to predict MTRs accurately from a knowledge of the pertinent tissue properties. Again, agreement with 1%–2% error is achieved (6). With this mechanistic understanding so well validated, it is possible to design optimal MT saturation pulse sequences for different clinical applications (7). The quantitative models of MT can even be used effectively to explore more complex pictures of NMR and tissue composition (8, 9). In using quantitative MTRs for evaluation of disseminated disease, maximum use should be made of the scientific underpinnings. Scattergrams and correlation can be left for exploring situations in which biophysical phenomena are less well understood.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.064 | 0.039 |
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".