Application of Telemedicine Based on a Digital Rehabilitation Platform in Patients with Cognitive Impairment After Spontaneous Cerebral Hemorrhage
Bibliographic record
Abstract
OBJECTIVE: To explore the application effect of a telemedicine method supported by a digital rehabilitation platform for patients with cognitive impairment after spontaneous cerebral hemorrhage. METHOD: Eighty eligible patients were enrolled in the current study from May 2023 to November 2024, with 40 patients each in the experimental and control groups. Both groups received the same routine treatment. In addition, the control group was given mobile intelligent devices and trained by the same nurses on how to perform the rehabilitation exercises. The experimental group was instructed on the precise treatment offered by the digital rehabilitation platform. RESULT: The Hamilton Anxiety Scale14, Hamilton Depression Scale 24, Montreal Cognitive Assessment, Minimum Mental State Examination, and modified Barthel score between the 2 groups were comparable at the time of enrollment. All indicators at the observation endpoint of the experimental and control groups (Hamilton Anxiety Scale 14, Hamilton Depression Scale 24, Montreal Cognitive Assessment, Minimum Mental State Examination, modified Barthel score, and patient satisfaction) were better than at the time of enrollment (intragroup). These indicators in the experimental group were better at the observation endpoint that the control group. CONCLUSION: The telemedicine medical care based on a digital rehabilitation platform achieved individualized, convenient, effective, and satisfactory rehabilitation feeling for spontaneous cerebral hemorrhage patients with concurrent cognitive impairment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".