IDDF2022-ABS-0272 Prevalence of frailty in cirrhotic- a observational study
Bibliographic record
Abstract
Background Frailty is increasingly recognized as a major prognostic factor in cirrhosis. Its assessment and intervention towards performance improvement is an important step in the management of liver cirrhosis. In this study, we aimed to assess the prevalence of frailty in patients with liver cirrhosis in the tertiary care center. Methods 93 patients were enrolled in this observational cohort study. Frailty assessment was done using compares Fried frailty index (FFI), Clinical frailty index (CFI), Short physical performance battery (SPPB), Edmonton frail scale (EFS), Liver Frailty Index (LFI), ECOG, Karnofsky performance scale (KPS) & Instrumental activity of daily living. The primary outcome was prevalence of frailty in cirrhotic patients, then its prevalence in different subgroups according to Etiology, CTP score & MELD. Results 73(81.1%) males and 13 (18.9%) females with a mean age of 43.89± 9.67 years were included. The most common cause of cirrhosis was alcoholic liver disease (47.7%) followed by hepatitis B (14.1%) and Hepatitis (7.8%). The prevalence of frailty based on LFI (46.67%), KPS (35.55%), FFI (38.9%), CFI (38.90%), SPPB (47.80%), EFI (31.20%), IDAL (31.10%), ECOG (36.70%). There was no significant difference in prevalence among different indexes (P value >0.05). Conclusions The prevalence of frailty based on LFI is 46.67%. LFI, KPS, FFI, CFI, SPPB, EFI, IDAL, ECOG are comparable in frailty assessment 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".