White matter integrity of watershed areas is potentially influenced by hypoperfusion in the presence permanent atrial fibrillation
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.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".