Advances in neuroRehabilitation of TREM2-related dementia
Bibliographic record
Abstract
RATIONABLE: The aim of this study is to investigate the effects of an advanced neuroRehabilitation protocol using virtual reality in the treatment of a patient with fronto- temporal dementia due to TREM2 mutation. PATIENTS CONCERN: A 41-year-old caucasian male, affected by Nasu-Hakola Disease (NHD), presented a 1-year history of change in behavioral and cognitive functioning, before our observation. The onset of the disease was characterized by severe pain in the lower limbs and knees with limitations in the performance of daily life activities. DIAGNOSIS: Motor and cognitive deficits in NHD. INTERVENTIONS: As the patient was in a chronic phase, to manage his cognitive and motor status, we decided to treat him by using a specific rehabilitation protocol, including 2 different types of training: conventional cognitive and motor treatment and a combined advanced approach using the virtual reality rehabilitation system (VRRS). The two protocols were separated by 4 weeks of rest, to avoid/reduce a cumulative effect. The patient's cognitive and motor profile was assessed three times: that is before (at T0) and after (at T1) the conventional training as well as at the beginning (T2) and at the end of the combined experimental approach (T3). OUTCOMES: After the combined therapeutic approach with the virtual reality rehabilitation system, we observed a significant reduction in anxiety, apathy, indifference and depressive symptoms with a more evident motor improvement involving the head and the trunk control. LESSONS: Virtual reality can be considered a promising tool for the motor and cognitive rehabilitation of rare neurodegenerative disorders, including patients with NHD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".