Depression in cirrhosis – a prospective evaluation of the prevalence, predictors and development of a screening nomogram
Bibliographic record
Abstract
BACKGROUND: Depression is associated with substantial morbidity and mortality in cirrhosis, but is underdiagnosed and undertreated. AIMS: Using the Mini International Neuropsychiatric Interview (MINI) as a gold-standard, to determine prevalence, predictors, and outcomes of depression, and to develop a screening nomogram for use in cirrhosis patients. METHODS: Cirrhotic outpatients 18-80 years of age, not on anti-depressants, were consecutively recruited from liver clinics at three tertiary care hospitals. Baseline health-related quality of life (HRQoL) and frailty were determined by the chronic liver disease questionnaire, EQ-VAS, Clinical Frailty Scale and Fried Frailty Criteria. Depression was identified using the MINI and participants were followed up to 6 months to determine unplanned hospitalization/death. RESULTS: Of 305 patients, 62% were male; mean age 55(10) years; mean MELD 12.5(5), 61% Child Pugh B/C. Prevalence of depression 18% by MINI. Patients with depression had lower baseline HRQoL and higher frailty scores. Five independently predictive factors were used to develop a clinical nomogram for the diagnosis of clinical depression. These included three Hospital Anxiety and Depression Screening tool variables: "I have lost interest in my appearance" (adjusted odds ratio [aOR] 2.2, P = 0.006), "I look forward with enjoyment to things" (aOR 2.0, P = 0.02), "I feel cheerful" (aOR 2.8, P = 0.002), and two demographic variables: younger age (aOR 0.92, P = 0.001) and not being married or in a common-law relationship (aOR 0.30, P = 0.008). CONCLUSIONS: Depression is common in patients with cirrhosis. It has a significant impact on HRQoL and functional status. The developed clinical nomogram is promising for the rapid screening of depression in patients 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".