MELD score, insulin-like growth factor 1 and cytokines on bone density in end-stage liver disease
Bibliographic record
Abstract
AIM: To determine the contributions of insulin-like growth factor 1 (IGF-1), cytokines and liver disease severity to bone mineral density in patients pre-transplantation. METHODS: Serum IGF-1, tumor necrosis factor-α (TNFα) and interleukin 6 (IL-6) were measured and the Model for End-Stage Liver Disease (MELD) score calculated in 121 adult patients referred to a single centre for liver transplantation. Bone mineral density (BMD) of the lumbar spine and femoral neck were assessed via dual energy X-ray absorptiometry. Demographics, liver disease etiology, medication use and relevant biochemistry were recorded. RESULTS: A total of 117 subjects were included, with low BMD seen in 68.6%, irrespective of disease etiology. In multivariable analysis, low body mass index (BMI), increased bone turnover and low IGF-1 were independent predictors of low spinal bone density. At the hip, BMI, IGF-1 and vitamin D status were predictive. Despite prevalent elevations of TNFα and IL-6, levels did not correlate with degree of bone loss. The MELD score failed to predict low BMD in this pre-transplant population. CONCLUSION: Osteopenia/osteoporosis is common in advanced liver disease. Low serum IGF-1 is weakly predictive but serum cytokine and MELD score fail to predict the severity of bone disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".