Cognitive profiles in severe post‐COVID 19 patients in a rehabilitation center
Bibliographic record
Abstract
Abstract Background After the start of the COVID‐19 pandemic, it has been described that many patients evolve with neurological alterations, including cognitive deficits. Method All severe post‐COVID patients admitted to hospitalization for rehabilitation between October 1, 2020 and July 30, 2021 were included. Those with central neurological pathology, altered state of consciousness, or psychiatric history were excluded. An abbreviated cognitive assessment was performed, including MoCa, CVLT, logical memory, forward and reverse digit span, IFS, TMT, Beck Depression Inventory. Result 196 severe post‐COVID patients were included, of which 76 did not present exclusion criteria and were able to perform the complete cognitive evaluation. The mean age was 60.8 years, 65% male. The average schooling in years was 12. 67% showed poor performance in the initial screening test; 6.58% presented an amnesic profile; 7.89% a dysexecutive profile; 34.21% showed multidomain failures; 21% presented a performance within normality. The remaining patients presented isolated cognitive failures, without being able to characterize a type of profile. 5.26% had a Beck score compatible with depression. Conclusion In our sample, 79% of the patients showed some type of cognitive alteration. We found a low percentage of patients with depression, contrary to what is described in the literature. We consider that the investigation of cognitive alterations and the possibility of defining a type of profile is extremely useful in order to be able to optimize all the rehabilitation work with the possibility of improving the results, social reinclusion and quality of life of patients.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".