Personalized Medicine for Chronic Diseases Through the Integration of Health Determinants Control in Patients: A Systematic Review
Bibliographic record
Abstract
Background: Chronic disease significantly contributes to global healthcare demands and costs. Despite these chronic illnesses, good health is achievable through public health strategies that enhance control over health determinants. This systematic review investigates how control over health determinants affects the health status of individuals with chronic diseases. Objective: To assess the impact of limited control over health determinants on health status in people with chronic diseases and identify potential clinical applications. Methods: A systematic review was conducted following PRISMA 2020 and COSMOS-E guidelines. Searches across five databases (PubMed, Google Scholar, ScienceDirect, CINAHL, PsycARTICLES) between February and April 2023 identified cohort studies published in the last 10 years. Studies involving individuals aged 16 years and older with at least one chronic disease were included. The Newcastle–Ottawa scale was used to assess study quality. Results: Four cohort studies (n = 576) were included, involving participants with chronic diseases such as COPD, diabetes, and Parkinson’s disease. The methodological quality averaged 6/9. Significant correlations were observed between control over four health determinant domains—social, behavioral, biological, and healthcare system—and declining health outcomes. Common biases included detection and comparability bias. Discussion: The studies had acceptable methodological quality and low external bias risks. However, the meta-analysis was compromised due to the heterogeneity observed in the exposure variables of the included articles. The review emphasizes the importance of integrating control over health determinants into patient care, with healthcare professionals positioned to enhance patient control and improve outcomes. Conclusions: Lack of control over health determinants, particularly in social and behavioral domains, correlates with poorer health outcomes in patients with chronic conditions. Assessing and improve healthcare control could identify high-risk patients and improve their quality of life.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.000 | 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 teacher head, 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".