Cerebral hypoxia in chronic kidney disease and its relation to cognitive decline
Bibliographic record
Abstract
Cerebral hypoxia in chronic kidney disease and its relation to cognitive decline Dissertation abstract - MUDr. Lucie Kalendová Introduction: Patients with chronic kidney disease in need of regular hemodialysis treatment have high rates of cognitive impairment. In its multifactorial etiology, vascular changes, cerebral ischemia and hypoxia play a major role. In our work we first studied the association between low cerebral oxygenation and cognitive impairment in this population. Subsequently, we focused on one of the possible etiological factors in this association - the presence of a vascular shunt for hemodialysis. Methods: Chronic hemodialysis patients without overt cognitive impairment participated in the studies. We used a near-infrared spectroscopy (NIRS) device named INVOS for monitoring cerebral oxygenation (rSO2). Cognitive function was assessed with the Montreal Cognitive Assessment (MoCA). To assess the effect of vascular shunt, we performed an interventional study based on short-term ultrasound-confirmed manual compression with continuous monitoring of rSO2. Results: In 39 patients (49 % women, age 64 ± 14 years) we observed a significantly lower rSO2 in the subgroup presenting cognitive decline than in patients without this diagnosis (48 ± 9 vs. 57 ± 10; p = 0.01). The association remained...
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".