Impact of multidisciplinary chronic disease collaboration management on self-management of hypertension patients: A cohort study
Bibliographic record
Abstract
To explore the effect of the interdisciplinary chronic disease management (CDM) model on patients with hypertension. In this intervention study, the subjects were divided into CDM and control groups. Blood pressure control was monitored in both groups. After 1 year of follow-up, the endpoint events of patients and their knowledge, confidence, and behavior in response to the disease were assessed. When compared with the control group, patients in the CDM group obtained higher scores for self-perception and management assessment, and their blood pressure control was also better after discharge. The quality of life and the satisfaction level of patients in the control group were lower than those in the CDM group, while the unplanned readmission rate, incidence of complications, and the average length of hospital stay in the control group were higher than those in the CDM group. CDM model was beneficial to blood pressure control in hypertensive patients. It had also improved the quality of life and the satisfaction level of the hypertensive patients. Our study highlights the importance of the CDM model in the prognosis of hypertensive patients.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".