Nonalcoholic fatty liver disease and aging: Epidemiology to management
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is common in the elderly, in whom it carries a more substantial burden of hepatic (nonalcoholic steatohepatitis, cirrhosis and hepatocellular carcinoma) and extra-hepatic manifestations and complications (cardiovascular disease, extrahepatic neoplasms) than in younger age groups. Therefore, proper identification and management of this condition is a major task for clinical geriatricians and geriatric hepatologists. In this paper, the epidemiology and pathophysiology of this condition are reviewed, and a full discussion of the link between NAFLD and the aspects that are peculiar to elderly individuals is provided; these aspects include frailty, multimorbidity, polypharmacy and dementia. The proper treatment strategy will have to consider the peculiarities of geriatric patients, so a multidisciplinary approach is mandatory. Non-pharmacological treatment (diet and physical exercise) has to be tailored individually considering the physical limitations of most elderly people and the need for an adequate caloric supply. Similarly, the choice of drug treatment must carefully balance the benefits and risks in terms of adverse events and pharmacological interactions in the common context of both multiple health conditions and polypharmacy. In conclusion, further epidemiological and pathophysiological insight is warranted. More accurate understanding of the molecular mechanisms of geriatric NAFLD will help in identifying the most appropriate diagnostic and therapeutic approach for individual elderly patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".