Bibliographic record
Abstract
Neurologists and neuroradiologists often see small T2-hyperintense lesions in the hemispheric white matter of patients who undergo MR imaging. Such findings often lead to neurologic consultation, particularly when the reason for MR imaging was not to study the white matter. While these “unidentified bright objects” have often been considered unimportant (usually when few in number), such lesions are a bad omen, predictive of stroke and dementia. Most commonly these lesions have a vascular etiology, and yet because we cannot image the small arteries directly we do not yet understand the nuances of leukoaraiosis.1,2 The study by Deary et al. in this issue of Neurology takes advantage of psychometric data from the Scottish Mental Survey of 1932.3 Forty nondemented patients underwent repeat cognitive testing 70 years later. As well, all had MRI, including diffusion tensor, magnetization transfer, and T2-weighted sequences. The principal result was that the fractional anisotropy (FA) measurements taken in the centrum semiovale were modestly but significantly correlated with current cognitive function (at a …
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".