Global slowness and increased intra-individual variability are key features of attentional deficits and cognitive fluctuations in post COVID-19 patients
Bibliographic record
Abstract
Fatigue, attentional deficits and cognitive fluctuations are the most characterizing symptoms of neurological involvement in Post COVID-19 syndrome (PCS). As the intraindividual variability (IIV) in cognitive performances has been recognized as a hallmark of brain-related disorders associated with cognitive deficits, it could be an interesting measure to elucidate the mechanisms subtending both the attentive impairment and the cognitive fluctuations in these patients. By referring to IIV analysis of Reaction Times (RTs), the present study aims to define the attentive impairment and its relation to fluctuations and fatigue, in patients suffering from Post COVID-19 neurological symptoms. 74 patients were enrolled. They underwent an extensive clinical and neuropsychological assessments, as well as computerized Sustained Attention and Stroop tasks. For studying IIV, RTs distributions of performances in computerized tasks were fitted with ex-Gaussian distribution, for obtaining the τ values. Finally, the Resting Motor Threshold (RMT) was also collected to estimate cortical excitability. 29 healthy volunteers served as controls. Patients showed poorer scores in Montreal Cognitive Assessment and higher RMT, in comparison with controls. In Sustained Attention Task, Mean, µ, σ and τ values were significantly higher in PCS patients (p value = < 0.0001; 0.001; 0.018 and < 0.0001, respectively). Repeated measures ANOVA comparing the RTs mean in Stroop task within-subject and between-subjects revealed significant condition and group effect (p < 0.0001 both) and significant interaction (p = 0.005), indicating worst performances in patients. The mean of the derived interference value was significantly higher in PCS patients than in controls (p = 0.036). Patients suffering from PCS show deficits in attention, both in the sustained and executive components. Both high RTs means and high IIV subtend these deficits and could explain the often-complained cognitive fluctuations in this population.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".