Can the choice reaction time be modified after COVID-19 diagnosis? A prospective cohort study
Bibliographic record
Abstract
Assessment of cognitive processing speed through choice reaction time (CRT) can be an objective tool to assess cognitive functions after COVID-19 infection. Objective: This study aimed to assess CRT in individuals after acute COVID-19 infection over 1 year. Methods: We prospectively analyzed 30 individuals (male: 9, female: 21) with mild-moderate functional status after COVID-19 and 30 individuals (male: 8, female: 22) without COVID-19. Cognitive and neuropsychiatric symptoms were evaluated using the Montreal Cognitive Assessment (MoCA) and Hospital Anxiety and Depression Scale (HADS), respectively. CRT (milliseconds) was evaluated by finding the difference between the photodiode signal and the electromyographic (EMG) onset latency of anterior deltoid, brachial biceps, and triceps during the task of reaching a luminous target. CRT was evaluated three times over 1 year after COVID-19: baseline assessment (>4 weeks of COVID-19 diagnosis), between 3 and 6 months, and between 6 and 12 months. Results: The multiple comparison analysis shows CRT reduction of the anterior deltoid in the COVID-19 group at 3-6 (p=0.001) and 6-12 months (p<0.001) compared to the control group. We also observed CRT reduction of the triceps at 6-12 months (p=0.002) and brachial biceps at 0-3 (p<0.001), 3-6 (p<0.001), and 6-12 months (p<0.001) in the COVID-19 compared to the control group. Moderate correlations were observed between MoCA and CRT of the anterior deltoid (r=-0.63; p=0.002) and brachial biceps (r=-0.67; p=0.001) at 6-12 months in the COVID-19 group. Conclusions: There was a reduction in CRT after acute COVID-19 over 1 year. A negative correlation was also observed between MoCA and CRT only from 6 to 12 months after COVID-19 infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".