Magnetic resonance white matter changesin patients with obstructive sleep apnoea.The subset of the PURE-MIND (Prospective Urbanand Rural Epidemiological) cohort study
Bibliographic record
Abstract
Introduction: Changes typical for cerebral small vessel disease (i.a. white matter hyperintensities - WMHs) are often found accidentally in neuroimaging studies. Although asymptomatic, this condition increases the risk of future ischaemic incidents and neurodegenerative disorders. Sleep apnoea is a recognised risk factor for vascular diseases. The aim of our study was to assess the prevalence of, and association between, obstructive sleep apnea (OSA) and cerebral small vessel disease in the studied population. Material and methods: Two hundred and seven patients (participants of Prospective Urban Rural Epidemiology Study) took part in our study. The study group consisted of 31 patients with OSA (11 women and 20 men). Nine of them were diagnosed with mild OSA, 9 with moderate OSA, and 13 with severe OSA. The control group consisted of 176 patients (133 women and 43 men) who scored 0-2 points in the STOP-BANG questionnaire. All patients underwent brain magnetic resonance imaging. We evaluated the occurrence and severity of WMHs. Results: = 0.00580). In univariate analyses, age was a significant predictor of periventricular white matter changes. For subcortical area, age and waist-to-hip ratio were significant predictors. Conclusions: Significantly higher incidence of WMHs in the studied group suggests that patients with OSA may have a higher risk of neurodegeneration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".