MELD score, insulin-like growth factor 1 and cytokines on bone density in end-stage liver disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it