Caffeine against persistent fatigue in long-COVID: a randomized clinical trial
Bibliographic record
Abstract
Background & Objective Long COVID causes persistent symptoms, such as fatigue and cognitive impairment, which caffeine may help mitigate. This study aimed to evaluate the acute effects of caffeine on these symptoms.Methods This randomized, double-blind, placebo-controlled trial with a within-subjects design was conducted between October 2021 and January 2023 in Araranguá, Brazil. Twenty-eight participants (13 caffeine, 15 placebo) received a single administration of either caffeine (3 mg/kg) or placebo capsules. Participants met the Myalgic Encephalomyelitis: International Consensus Criteria, with fatigue assessed using the Chalder Fatigue Questionnaire. The cohort was predominantly of females (64.3%), with a mean age of 44.6 ± 7.7 years. Most were sedentary (82.1%) with moderate baseline fatigue. Exclusion criteria included significant cardiovascular or respiratory diseases. Primary outcomes included the Incremental Shuttle Walk Test, Borg Scale, Short Physical Performance Battery, Stroop Test, Montreal Cognitive Assessment, and biochemical markers. Data are presented as mean (standard deviation) or median [interquartile range, IQR], with statistical analyses performed using independent t-tests, Mann–Whitney U tests, and effect size calculations according to data distribution. Statistical significance was set at p < 0.05.Results Caffeine increased walking distance (476 [70.1] m vs. 284.4 [83] m; p < 0.05) and improved executive function (Stroop test: 47.3 [IQR 43.2–54.2] vs. 68.2 [IQR 60.9–72.2]; p < 0.05). Perceived exertion decreased pre- and post-exercise (p < 0.05).Conclusion Caffeine alleviates fatigue and cognitive dysfunction in Long COVID, supporting its potential role in rehabilitation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".