Prevalence and predicting factors of caregiver burden in cirrhotic patients
Bibliographic record
Abstract
Background: Cirrhosis is a major cause of morbidity and mortality. Caregivers of cirrhotic patients provide significant support related to disease manifestations and complications. This scoping review aims to identify the prevalence of caregiver burden among patients with cirrhosis and to identify patient and caregiver factors that predict caregiver burden in patients with cirrhosis. Methods: A literature search was conducted using MEDLINE, EMBASE, CINAHL, and Web of Science to identify studies for inclusion. Screening and data extraction were performed using Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). Results: 604 articles were identified, and 15 were included in the review. Caregivers were predominantly female and spouses of the patient. The average age of patients varied between 41.2 and 57.2 (SD 10.3 to 12.5). The most common cirrhotic aetiologies were alcohol-related, viral hepatitis-related, and metabolic-related aetiologies. The most used survey tools to assess burden were the Zarit Burden Interview (ZBI) score, Short Form-36 Health Survey (SF-36), and Beck Depression/Anxiety Inventory (BDI or BAI). Patient factors contributing toward caregiver burden included prior hepatic encephalopathy (HE), alcohol use, and high Model for End-Stage Liver Disease (MELD) scores. Caregiver factors that contribute toward caregiver burden included poor perceived social supports, low and disrupted income, and being the spouse of the patient. Conclusions: Several patient and caregiver factors contribute to caregiver burden, and greater levels of burden may affect multiple aspects of a caregiver's life, potentially worsening patient outcomes. A multidisciplinary approach is critical to alleviate caregiver burnout and optimize the overall care for a patient with cirrhosis.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".