Association between the malnutrition-inflammation score and post-transplant anaemia
Bibliographic record
Abstract
BACKGROUND: Post-transplant anaemia (PTA) is common and is associated with adverse consequences. The protein-energy wasting (PEW) syndrome is associated with erythropoietin resistance in patients on maintenance dialysis. We assessed the association between PEW and PTA in a large prevalent cohort of stable kidney-transplanted patients. METHODS: Data from 942 prevalent kidney-transplanted patients were analysed. Socio-demographic parameters, laboratory results, transplantation-related data and medication were obtained from the charts. Biomarkers reflecting nutritional status and inflammation [serum leptin, albumin, interleukin-6 (IL-6), tumour necrosis factor-α (TNF-α) and C-reactive protein] were measured. Anthropometric measures and the malnutrition-inflammation score (MIS) were also tabulated. Anaemia was defined according to the guidelines of the American Society of Transplantation. RESULTS: Mean age was 51 ± 13 years, 57% were males and 22% had diabetes. The prevalence of PTA was 33%. The haemoglobin (Hb) level significantly and negatively correlated with the MIS (rho = - 0.316), marginally with serum TNF-α (rho = - 0.079) and serum IL-6 (rho = - 0.075) and positively with serum transferrin (r = 0.298), serum albumin (r = 0.274), abdominal circumference (r = 0.254) and serum leptin (rho = - 0.152), P < 0.05 for all. In a multivariable linear regression model, MIS was independently associated with Hb (beta = - 0.118, P = 0.004) in patients with estimated glomerular filtration rate (eGFR) lower than or equal to 60 mL/min/1.73 m(2), but not in patients with higher eGFR. CONCLUSIONS: The MIS is independently associated with PTA in the kidney-transplanted population with eGFR lower than or equal to 60 mL/min/1.73 m(2).
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".