Habitual Coffee Consumption Enhances Attention and Vigilance in Hemodialysis Patients
Bibliographic record
Abstract
OBJECTIVE: Coffee drinking is the main source of caffeine intake among adult population in the western world. It has been reported that low to moderate caffeine intake has beneficial effect on alertness and cognitive functions in healthy subjects. The aim of this study is to evaluate the impact of habitual coffee consumption on cognitive function in hemodialysis patients. METHODS: In a cross-sectional study, 86 patients from a single-dialysis centre underwent assessment by the Montreal Cognitive Assessment tool and evaluation for symptoms of fatigue, mood, and sleep disorders by well-validated questionnaires. The habitual coffee use and the average daily caffeine intake were estimated by participants' response to a dietary questionnaire. RESULTS: Sixty-seven subjects (78%) consumed black coffee daily, mostly in low to moderate dose. Cognitive impairment was found in three-quarters of tested patients. Normal mental performance was more often in habitual coffee users (25% versus 16%). Regular coffee drinkers achieved higher mean scores on all tested cognitive domains, but a significant positive correlation was found only for items that measure attention and concentration (P = 0.024). CONCLUSIONS: Moderate caffeine intake by habitual coffee consumption could have beneficial impact on cognitive function in hemodialysis patients due to selective enhancement of attention and vigilance.
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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 | 0.000 |
| 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) | 0.001 | 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".