Serum ferritin level changes in children with sickle cell disease on chronic blood transfusion are nonlinear and are associated with iron load and liver injury
Bibliographic record
Abstract
Chronic blood transfusion is increasingly indicated in patients with sickle cell disease. Measuring resulting iron overload remains a challenge. Children without viral hepatitis enrolled in 2 trials for stroke prevention were examined for iron overload (STOP and STOP2; n = 271). Most received desferrioxamine chelation. Serum ferritin (SF) changes appeared nonlinear compared with prechelation estimated transfusion iron load (TIL) or with liver iron concentrations (LICs). Averaged correlation coefficient between SF and TIL (patients/observations, 26 of 164) was r = 0.70; between SF and LIC (patients/observations, 33 of 47) was r = 0.55. In mixed models, SF was associated with LIC (P = .006), alanine transaminase (P = .025), and weight (P = .026). Most patients with SF between 750 and 1500 ng/mL had a TIL between 25 and 100 mg/kg (72.8% +/- 5.9%; patients/observations, 24 of 50) or an LIC between 2.5 and 10 mg/g dry liver weight (75% +/- 0%; patients/observations, 8 of 9). Most patients with SF of 3000 ng/mL or greater had a TIL of 100 mg/kg or greater (95.3% +/- 6.7%; patients/observations, 7 of 16) or an LIC of 10 mg/g dry liver weight or greater (87.7% +/- 4.3%; patients/observations, 11 of 18). Although SF changes are nonlinear, levels less than 1500 ng/mL indicated mostly acceptable iron overload; levels of 3000 ng/mL or greater were specific for significant iron overload and were associated with liver injury. However, to determine accurately iron overload in patients with intermediately elevated SF levels, other methods are required. These trials are registered at www.clinicaltrials.gov as #NCT00000592 and #NCT00006182.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".