Renal dysfunction in patients with thalassaemia
Bibliographic record
Abstract
Little is known about the effects of thalassaemia on the kidney. Characterization of underlying renal function abnormalities in thalassaemia is timely because the newer iron chelator, deferasirox, can be nephrotoxic. We aimed to determine the prevalence and correlates of renal abnormalities in thalassaemia patients, treated before deferasirox was widely available, using 24-h collections of urine. We calculated creatinine clearance and urine calcium-to-creatinine ratio and measured urinary β(2) -microglobulin, albumin, and protein. We used multivariate modelling to identify clinical, therapeutic, and laboratory predictors of renal dysfunction. One-third of thalassaemia patients who were not regularly transfused had abnormally high creatinine clearance. Regular transfusions were associated with a decrease in clearance (P = 0·004). Almost one-third of patients with thalassaemia had hypercalciuria, and regular transfusions were associated with an increase in the frequency and degree of hypercalciuria (P < 0·0001). Albuminuria was found in over half of patients, but was not consistently associated with transfusion therapy. In summary, renal hyperfiltration, hypercalciuria, and albuminuria are common in thalassaemia. Higher transfusion intensity is associated with lower creatinine clearance but more frequent hypercalciuria. The transfusion effect needs to be better understood. Awareness of underlying renal dysfunction in thalassaemia can inform decisions now about the use and monitoring of iron chelation.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".