Changes in cognitive impairment after multidimentional rehabilitation in Neuro COVID severe respiratory failure patients
Bibliographic record
Abstract
Abstract Background A Post Intensive Care Syndrome Unit (PICSU) was developed during COVID 19 pandemic. The aim of this study was to evaluate Cognitive Impairment (CI) evolution after PICSU treatment in Neuro COVID patients. Methods Prospective observational study. Population: Neuro COVID patients treated in a PICSU. CI was achieved by means of the Spanish version of the MoCA at ICU discharge and one month after, muscle function was evaluated by means of Medical Research Council test for Intensive Care Acquired Weakness (ICUAW). Statistical analysis was performed by ANOVA and Chi squared test. Age, sex, comorbidities, mechanical ventilation days and length of stay at the PICSU wre also measured. Patients were submitted to an intensive rehabilitation program that included physical, swallowing, speech therapy, occupational therapy ones, as well as nutritional and psychological support. Results 27 patients were admitted to PICSU, 5 were female, average age was 57 yo. 30% of them presented normal cognition. Regarding CI patients mean initial MoCA value was 18 and improved to 23 (p < 0.05), MRC values were 42 y 52 respectively (p < 0.05). According MoCA values using a cutoff point of 26 to determinate normal cognition, 16 to 25 for MCI and 15 for Mild Dementia, 26% had MCI and 74% at ICU discharge that evolved to normal (26%), MCI (58%) and MD (16%) (p < 0.05). There was a close relationship between MoCA and MCR enhancement, CI and ICUAW improved simultaneously (p < 0.05). Conclusion PICSU rehabilitation program was useful for cognition and motor function improvement. The close relationship between cognition and motor function could be explained by the myokines and neurotrophic factors release toward muscle activity. This could be an opportunity for future research.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".