The spatial coefficient of variation in arterial spin labeling cerebral blood flow images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Macro-vascular artifacts are a common arterial spin labeling (ASL) finding in populations with prolonged arterial transit time (ATT) and result in vascular regions with spuriously increased cerebral blood flow (CBF) and tissue regions with spuriously decreased CBF. This study investigates whether there is an association between the spatial signal distribution of a single post-label delay ASL CBF image and ATT. In 186 elderly with hypertension (46% male, 77.4 ± 2.5 years), we evaluated associations between the spatial coefficient of variation (CoV) of a CBF image and ATT. The spatial CoV and ATT metrics were subsequently evaluated with respect to their associations with age and sex - two demographics known to influence perfusion. Bland-Altman plots showed that spatial CoV predicted ATT with a maximum relative error of 7.6%. Spatial CoV was associated with age (β = 0.163, p = 0.028) and sex (β = -0.204, p = 0.004). The spatial distribution of the ASL signal on a standard CBF image can be used to infer between-participant ATT differences. In the absence of ATT mapping, the spatial CoV may be useful for the clinical interpretation of ASL in patients with cerebrovascular pathology that leads to prolonged transit of the ASL signal to tissue.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it