Cognitive impairment detected by MoCA (Montreal Cognitive Assessment) test after COVID-19 in Mexico
Bibliographic record
Abstract
Background and aims: The year 2020 was marked by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) pandemic, that causes the human coronavirus disease 2019 (COVID-19). Multiple neurological symptoms have been reported;however, there is scarce knowledge about their impact on cognitive functions in recovered patients. Methods: We report the results of the MoCA cognitive screening test (Mexican Version 7.3), applied by two neurologists to 242 patients who were admitted at a single medical center in Mexico City with acute respiratory distress syndrome (ARDS) due to COVID-19, three months after their hospital discharge, from August 31, 2020 to January 11, 2021. Results: All patients were positive for SARS-CoV-2, tested via reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays of nasopharyngeal samples. The mean age of the cohort was 52 years, 145 patients (59.9%) reported cognitive imparment by themselves or by their relatives. 171 (70.6%) patients had fewer than 12 years of schooling;three patients already had previous cognitive complaints;bradykinesia was found in two patients. The average score on the MoCA test was 25.5;37 patients scored between 22 and 14 points, and four patients scored less than 12 points. We requested brain magnetic resonance imaging and neuropsychological tests to all patients with cognitive complaint or with MoCA scores under 23 points. These studies are still in progress. Conclusion: Neurological manifestations in pandemics frequently cause long-term consequences which are frequently overlooked. The cognitive impairments found in the MoCA sreeening test in COVID-19 survivors may have a multifactorial origin, yet requires further evaluations and close long-term follow-up.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".