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 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 | 0.000 |
| 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 | 0.001 |
| 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".