The Impact of Long COVID on Cognitive Performance and Sleep Quality: An Analysis of the Rancagua Chilean Study (RACHIS)
Bibliographic record
Abstract
Background Infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to prolonged symptoms post-recovery, commonly known as long-term coronavirus disease 2019 (COVID-19) or "long COVID." Neuropsychiatric consequences of long COVID include cognitive dysfunction and sleep disturbances, which significantly impair daily living. This study aimed to explore the impact of long COVID on cognitive performance and sleep quality in patients receiving outpatient care. Material and methods This study involved a random sample of 138 of 363 patients, corresponding to 38% of the cohort, who tested positive for SARS-CoV-2 via polymerase chain reaction (PCR) between May 2020 and April 2021. These unvaccinated, non-hospitalized individuals, predominantly exhibiting mild disease symptoms, were prospectively assessed 11 months post-positive PCR test. After informed consent, demographic data, memory, and concentration impairment levels were collected through interviews. Participants reporting cognitive symptoms underwent the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MOCA), and the Pittsburgh Sleep Quality Index. Statistical analyses were conducted, including Student's t-test, Chi-square, Fisher's test, Kruskal-Wallis test, and Pearson correlation coefficient, with a significance threshold set at p<0.05. Results Of the 138 participants, 76 (55.1%) were female and 62 (44.9%) were male. The mean age was 45.9 years (± 13.0), with an average educational attainment of 10.4 years (± 3.7). Roughly 50% of the patients reported significant memory and concentration issues (p<0.001). Thirty-three participants underwent detailed cognitive assessments, revealing a 2:1 female-to-male ratio and a significantly higher prevalence of depression in female participants. Cognitive deficits were diagnosed in five (15.2%) participants via the MMSE and in 26 (78.8%) via the MOCA test, with notable deficits in visuospatial/executive functions, language repeat, and deferred recall (p<0.001). A lower educational level was correlated with higher cognitive deficits (p=0.03). Conclusion The study findings reveal that cognitive impairments, as a consequence of COVID-19, can persist up to 11 months post-infection. The MOCA test proved more effective in diagnosing these deficits and requires adjustments based on educational background. Sleep parameters remained largely unaffected in this cohort, likely attributed to the mild nature of the initial symptoms and the outpatient management of the disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".