White matter integrity of watershed areas is potentially influenced by hypoperfusion in the presence permanent atrial fibrillation
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
Aim. To test a hypothesis of hypoperfusion-induced white matter changes in patients with atrial fibrillation (AFib) and to present statistics to compute sample size for the upcoming studies. Material and methods. We included 30 inpatients with AFib and investigated them with magnetic resonance imaging (MRI) with standard sequencies and diffusion tensor imaging (DTI). DTI data were analyzed with conventional ROI analysis in the Olea Sphere software and with watershed areas (WSA) mask in the FSL toolbox after nonlinear transformation of images to the Montreal Neurological Institute (MNI) space. Wilcoxon test was used to compare diffusion characteristics across subgroups. Results. Median age of participants was 73 years (69-78), 18 (60%) patients had moderate signs of small vessel disease with Fazekas score of one. Twenty-one patients had paroxysmal AFib. Analysis of WSA revealed decreased white matter integrity in the parieto-occipital cortical WSA with a pattern of significantly increased mean diffusivity (p=0,039), and marginally significant decrease in fractional anisotropy (p=0,056). Rank-based effect size across areas under comparison was either small (0,2) or negligible, and with statistical power in the range of 0,05-1. Conclusion. Atrial fibrillation could have pathophysiologically feasible mechanism to affect white matter integrity in the watershed areas.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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