Quality Care Alleviates Behavioral Cognitive Impairment and Reduces Complications in Elderly Patients with Cardiovascular and Cerebrovascular Diseases
Post-publication record
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Bibliographic record
Abstract
Objective: Cardiovascular and cerebrovascular disease (CCVD) remains the most common factor of death around the world. Nursing care plays a key role in the recovery of patients with CCVD. This study was to explore the application of quality care in aged patients with CCVD. Methods: Totally, 74 aged CCVD patients admitted from June 2018 to June 2019 in Dongying People's Hospital were randomly assigned in 2 groups with the same treatment. The control group was treated with routine care intervention, and the observation group was treated with quality care intervention for 12 weeks. Meanwhile, the frequency of agitation behaviors and cognitive ability were assessed, and complication was counted. Results: The observation group showed decreased Cohen-Mansfield Agitation Inventory (CAMI) scores from 47.31 ± 8.27 to 38.73 ± 6.94, raised Mini-Mental State Examination (MMSE) scores from 15.01 ± 3.9 to 19.34 ± 3.15 and Montreal Cognitive Assessment (MoCA) scores from 16.92 ± 5.48 to 20.37 ± 4.16, and reduced complications after quality care intervention. Conclusion: Quality care intervention exerted a better application effect on aged CCVD patients, along with reduction of agitation, improvement of mental condition and behavioral cognitive function, and reduced complications.
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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 | 0.002 |
| 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".