Existence de différents profils de patients Covid Long ayant des plaintes cognitives
Bibliographic record
Abstract
Objectives. Following an infection with covid-19, a large number of patients experiences difficulties in the domains of memory, attentional and executive functioning (1). The origin of those cognitive complaints in long COVID patients is likely multifactorial and may be explained by an objective decline in cognitive performance but also by psychological or somatic factors. Furthermore, subgroups of patients have been identified in the literature (2). We aim to characterize long COVID patients according to their cognitive performance and perception of difficulties in daily life. Therefore, we seek first to determine whether there are different profiles of long COVID patients based on their cognitive performance, and second if each group is associated with specific difficulties in daily life. Methods. Data are from an ongoing randomized controlled trial (3). 123 patients (aged 47 ± 10 [range 21-66]; 39 males; time since infection: 20 months ± 8 [range 4-39]) with cognitive complaints following one or more Covid-19 infections were tested. A neuropsychological assessment was carried out to investigate global cognitive performance (MOCA), verbal and visuo-spatial long-term memory (RBANS, BVMT-R), processing speed, selective attention (D2-R, TAP), divided attention (TAP), inhibition (STROOP), verbal fluidity, flexibility (TAP) and working memory (BROWN-PETERSON, TAP). Factorial analysis was conducted on objective performance and latent profile analysis (LPA) allowed us to identify different profiles. Then, we examined whether these profiles differ on self-reported questionnaires addressing executive functioning (BRIEF-A), memory (MMQ), and difficulties in everyday life (fatigue (MFIS), quality of sleep (PSQI), psychological distress (OQ-45), quality of life (ISQV) and reduction in work and activity (WPAI)). Results. Factorial analysis revealed only two composite factors (an aspecific processing speed factor and an attentional selectivity factor), as well as three variables that were not related to these two factors (updating, BROWN-PETERSON and RBANS) which were converted into standardised values. An LPA analyses with these two factors and these three variables showed 3 profiles of long covid patients: a profile with severe impairment in all cognitive domains, a profile with milder impairment, and a profile with specific memory impairment. Boostrap version on one-way robust ANOVAs revealed that the severely impacted profile has higher complaints about their executive functioning and memory capacities, and also reports higher cognitive and physical fatigue. Remarkably, the profile with specific memory impairment has the lowest complaints, including on the memory questionnaire. All participants, regardless of their profile, have a low self-reported quality of live, high psychological distress and a strong overall impact on their daily activities. Conclusions. These exploratory results suggest that there are different profiles of individuals reporting cognitive symptoms after a Covid-19 infection. Accordingly, the public health response to long COVID condition should be adapted and focused on their specific difficulties (i.e., memory-related or diffuse). As decrease in well-being and quality of life seems to affect all long COVID patients, an integrative approach that focuses on both cognitive and affective aspects should be favoured in rehabilitation programs. We also highlight the critical issue of oversimplifying the disease and the long COVID patients. REFERENCES (1) Jaywant et al. 2021 Neuropsychopharmacology (2) Voruz et al. 2022 Brain Communications (3) Willems et al. 2023 BMC neurology
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".