Methylphenidate for the Treatment of Post-COVID Cognitive Dysfunction (Brain Fog)
Bibliographic record
Abstract
A substantial number of patients develop cognitive dysfunction after contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), significantly contributing to long-coronavirus disease (COVID) morbidity. Despite the urgent and overwhelming clinical need, there are currently no proven interventions to treat post-COVID cognitive dysfunction (PCCD). Psychostimulants like methylphenidate may enhance both noradrenergic and dopaminergic pathways in mesolimbic and pre-frontal areas, thus improving memory and cognition. We present a case series of six patients who were treated at the Johns Hopkins Post-Acute COVID-19 Team (PACT) clinic for PCCD with methylphenidate 5 - 20 mg in the context of routine clinical care and followed for 4 to 8 weeks. Baseline and post-treatment outcomes included subjective cognitive dysfunction and objective performance on a battery devised to measure cognitive dysfunction in long-COVID patients. Three out of the six patients reported subjective improvement with methylphenidate, one patient described it as "notable" and another as "marked" improvement in memory and concentration. We also found significant pre-treatment subjective complaints of cognitive dysfunction; however, formal cognitive assessment scores were not severely impaired. A statistically significant difference in pre and post scores, favoring intervention, was found for the following cognitive assessments: Hopkins verbal learning test (HVLT) immediate recall, HVLT delayed recall and category-cued verbal fluency. The current series demonstrates promising neurocognitive effects of methylphenidate for long-COVID cognitive impairment, particularly in recall and verbal fluency domains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| 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 teacher head, 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".