Risk factors for hyperferritinemia secondary to red blood cell transfusions in pediatric cancer patients
Bibliographic record
Abstract
BACKGROUND: Transfusion of packed red blood cells is common in pediatric cancer patients who receive chemotherapy. This study was done to identify characteristics of pediatric cancer patients at risk of hyperferritinemia secondary to frequent transfusions. PROCEDURE: In this retrospective chart review, all pediatric cancer patients who completed chemotherapy from January 2007 to January 2012 and had an assessment of serum ferritin 6 months after the end of treatment were included. Variables included: age, sex, type of cancer diagnosis, weight and body surface area (BSA) at the time of diagnosis, number of transfusions, total transfused volume (TTV), total transfused volume per body weight (TVPBW), and weight and BSA change from the time of diagnosis to the time of ferritin check. RESULTS: Of 109 eligible patients, 85 (78%) received transfusions. Sixteen patients (14.7%) had ferritin levels > 200 µg/L and four (3.7%) had ferritin levels > 1,000 µg/L. Although age, weight and BSA at cancer diagnosis, number of transfusions and TVPBW were correlated with the level of ferritin, independent risk factors were TTV (range 1,961-30,090 ml in patients with hyperferritinemia, P < 0.001) and BSA change from the time of diagnosis to the time of ferritin check (range -0.15 to 0.31 m(2) in patients with hyperferritinemia, P < 0.001). Increase in BSA was correlated with reduction of hyperferritinemia in follow-up ferritin measurements (P = 0.049). CONCLUSIONS: In addition to TTV, change in BSA is an independent predictor for the degree and possibly persistence of hyperferritinemia in pediatric cancer patients and should be considered in decisions to initiate interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".