Cognitive Assessment of Patients with COVID-19 Disease After Their Intensive Care Unit Stay
Bibliographic record
Abstract
Background: Severe ARDS (Acute Respiratory Distress Syndrome) patients are prone to develop neurocognitive deficit in the course of their intensive care stay. Indeed, a prevalence of 32% for all subjects >50 years has been reported. However, as ARDS is a syndrome, etiologies were various and by definition non-homogenous. Nonetheless, it is unknown if this proportion will be the same in a more homogenous cohort of patients as encountered in this pandemic due to SARS Cov-2. Therefore, neurocognitive evaluation was performed in surviving ARDS patients to evaluate the presence of eventual deficit. Materials and Methods: All ARDS patients admitted to our ICU, after given their inform consent, were included in the study. To assess their neurocognitive status, MoCA (Montreal Cognitive Assessment) score and the SDMT (Symbol Digit Modalities Test) test were used as screening tools and performed by a trained neuropsychologist. When a neuropsychological deficit was clinically suspected, complete neuropsychological tests were performed instead. The tests were carried out three month after ICU admission, and if pathological, repeated at six. If still positive, full tests were suggested and followed by appropriate treatment if accepted. Results: There were 12 patients (8 males) with a mean age of 60 y.o (range 52-73). Ten were screened and 2 carried out complete tests. Among the ten, screening tests revealed a deficit in 4 (40% of patients, 4 MoCA score 50 years admitted to intensive care regardless of the initial pathology. Therefore, the initial hypothesis that a reversible homogenous viral cause of ARDS could decrease the proportion of neurocognitive deficit is not confirmed. Thus, cognitive alteration could be more related to sedation than to the initial illness. Further studies are required to valid this hypothesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".