Regression-based Reference Limits for Serum Transferrin Receptor in Children 6 Months to 16 Years of Age
Bibliographic record
Abstract
Serum soluble transferrin receptor (sTfR) has been established in recent years as a powerful tool for detecting iron deficiency (ID) in adults, especially in distinguishing between iron deficiency anemia (IDA) and anemia of chronic disease (1)(2)(3)(4)(5)(6)(7)(8). Investigations regarding sTfR as a measure of iron status in infants and children have provided promising results, including evidence that, in infants, sTfR concentrations may be superior to ferritin measurements in diagnosing ID (9). However, to date, concrete reference values and other decision-supporting limits for the commercially available methods have been virtually absent, and the age-relatedness of sTfR concentrations, although introduced as a concept, has not been unequivocally modeled statistically (10)(11)(12)(13)(14)(15). In this study we measured the sTfR concentrations from a selection of 301 healthy children, 6 months to 18 years of age, using a commercially available automated immunoturbidimetric assay. We then used a regression-based method to construct age-dependent 2.5% and 97.5% reference limits for sTfR as well as 95% confidence intervals for these limits in our population (16). The purpose was to demonstrate consistent age-dependent changes in sTfR concentrations and to establish appropriate reference limits to enable the use of sTfR measurements in routine pediatric clinical practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".