5 Cognitive Rehabilitation Using Teleneuropsychology. A Cohort Study in South America
Bibliographic record
Abstract
Objective: The COVID-19 pandemic has affected the continuity of cognitive rehabilitation (CR) worldwide. However, the use of teleneuropsychology (TNP) to provide CR has contributed significantly to the continuity of treatment. The objective of this study was to measure the effects of CR via the TNP on cognition, neuropsychiatric symptoms, and memory strategies in a cohort of patients with Mild Cognitive Impairment (MCI). Participants and Methods: A sample of 60 patients (60% female; age: 72.4±6.96) with MCI according to Petersen criteria was randomly divided into two groups: 30 cases (treatment group) and 30 controls (waiting list group). Subjects were matched for age, sex, and MMSE or MoCA. The treatment group received ten weekly CR sessions of 45 minutes weekly. Pre-treatment (week 0) and post-treatment (week 10) measures were assessed for both groups. Different Linear Mixed Models were estimated to test treatment effect (CR vs. Controls) on each outcome of interest over Time (Pre/Post), controlling for Diagnosis, Age, Sex, and MMSE/MoCA performance. Results: A significant Group (Control/Treatment) x Time (pre/post) interaction revealed that the treatment group at 10 weeks had better scores in cognitive variables: memory (RAVLT learning trials p=0.030; RAVLT delayed recall p=0.029), phonological fluency(p=0.001), activities of daily living (FAQ p=0.001), satisfaction with memory performance (MMQ Satisfaction p=0.004) and use of memory strategies (MMQ Strategy p=0.00), and a significant reduction of affective symptomatology: depression (GDS p=0.00), neuropsychiatric symptoms (NPIQ p=0.045), Forgetfulness (EDO-10 p=0.00), Stress (DAS Stress p=0.00). Conclusions: This is the first study to test CR using teleNP in South America. Our results suggest that CR through teleNP is an effective intervention to improve performance on cognitive variables and reduce neuropsychiatric symptomatology compared to patients with MCI. These results have great significance in the context of the COVID-19 pandemic in South America, where teleNP is proving to be a valuable tool.
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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 | 0.000 | 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".