Bibliographic record
Abstract
We read with interest the letter in this issue by Hall-Craggs and colleagues1 discussing the developing field of diffusion-weighted imaging (DWI) and its application for quantification of inflammation in rheumatology. As proponents of quantification in medical imaging, we agree that DWI does indeed have some advantages. Its ability to objectively quantify diffusivity of water molecules in the brain was well established many years ago2 and is now absolutely essential for magnetic resonance imaging (MRI) of stroke, providing unique information about brain cell injury due to hypoxia. DWI is now often used for detection and characterization of tumors3. However, its application for assessment of inflammation outside the brain is at an earlier stage of development. One potentially exciting application may be the assessment of synovitis, where conventional sequences have difficulty distinguishing inflamed synovium from effusion without intravenous (IV) injection of a contrast agent. If DWI turns out to be a suitable substitute for the IV injection4, it would be … Address correspondence to Prof. R.G. Lambert, Department of Radiology and Diagnostic Imaging, University of Alberta, 2A2.WMC, 8440-112 St., Edmonton, Alberta T6G 2B7, Canada. E-mail: rlambert{at}ualberta.ca
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.033 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".