Brain hypoxia, neurocognitive impairment, and quality of life in people post-COVID-19
Bibliographic record
Abstract
Abstract Objective Systemic hypoxia occurs in COVID-19 infection; however, it is unknown if cerebral hypoxia occurs in convalescent individuals. We have evidence from other conditions associated with central nervous system inflammation that hypoxia may occur in the brain. If so, hypoxia could reduce the quality of life and brain function. This study was undertaken to assess if brain hypoxia occurs in individuals after recovery from acute COVID-19 infection and if this hypoxia is associated with neurocognitive impairment and reduced quality of life. Methods Using frequency-domain near-infrared spectroscopy (fdNIRS), we measured cerebral tissue oxygen saturation (StO2) (a measure of hypoxia) in participants who had contracted COVID-19 at least 8 weeks prior to the study visit and healthy controls. We also conducted neuropsychological assessments and health-related quality of life assessments, fatigue, and depression. Results Fifty-six percent of the post-COVID-19 participants self-reported having persistent symptoms (from a list of 18), with the most reported symptom being fatigue and brain fog. There was a gradation in the decrease of oxyhemoglobin between controls, and normoxic and hypoxic post-COVID-19 groups (31.7 ± 8.3 μM, 27.8 ± 7.0 μM and 21.1 ± 7.2 μM, respectively,p = 0.028,p = 0.005, andp = 0.081). We detected that 24% of convalescent individuals’ post-COVID-19 infection had reduced StO2in the brain and that this relates to reduced neurological function and quality of life. Interpretation We believe that the hypoxia reported here will have health consequences for these individuals, and this is reflected in the correlation of hypoxia with greater symptomology. With the fdNIRS technology, combined with neuropsychological assessment, we may be able to identify individuals at risk of hypoxia-related symptomology and target individuals that are likely to respond to treatments aimed at improving cerebral oxygenation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".