Application of nursing centered mobile APP in the rehabilitation of cognitive dysfunction patients after cerebral stroke
Bibliographic record
Abstract
Objective To explore the application effects of nursing centered mobile APP service platform in the rehabilitation of cognitive dysfunction patients after cerebral stroke, so as to provide a theoretical basis for its clinical promotion. Methods A total of 204 patients with cognitive impairment after cerebral stroke in Beijing Friendship Hospital Affiliated to the Capital Medical University from December 2012 to December 2014 were selected by convenience sampling method, and randomly divided into control group and observation group, with 102 cases in each. The control group was treated with conventional oral medication and routine rehabilitation training. On the basis of the treatment of the control group, nursing centered mobile APP service platform was applied in the observation group. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were used to evaluated the patients' outcome of rehabilitation and daily living activity at the time points of before treatment, discharge, the 3rd month and 12th month after the intervention. Results The interaction differences of the main effect of time, the main effect of intervention, intervention and time among the score of MMSE, MoCA and modified Barthel index were statistically significant (P<0.05) . Conclusions The nursing centered APP mobile service platform can improve the patients' cognitive function after stroke and the ability of daily activities, with a certain degree of clinical promotion value. Key words: Daily living activities; Cognition disorders; Mobile phone AAP
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".