Using Soluble Transferrin Receptor and Taking Inflammation into Account When Defining Serum Ferritin Cutoffs Improved the Diagnosis of Iron Deficiency in a Group of Canadian Preschool Inuit Children from Nunavik
Bibliographic record
Abstract
The prevalence of iron depletion, iron deficient erythropoiesis (IDE), and iron deficiency anemia (IDA) was assessed in preschool Inuit children using soluble transferrin receptor (sTfR) and traditional indicators of iron status while disregarding or taking inflammation into account when defining SF cutoffs. Iron depletion was defined as follows: (1) SF < 15 μg/L regardless of the C-reactive protein (CRP) level and (2) SF < 15 or <50 μg/L with CRP ≤ 5 or >5 mg/L, respectively. IDE corresponded to iron depletion combined with total iron binding capacity > 72 μmol/L and/or transferrin saturation < 16%. Iron depletion and IDE affected almost half of the children when accounting for inflammation, compared to one-third when the SF cutoff was defined regardless of CRP level (P < 0.0001). The prevalence of IDE adjusted for inflammation (45.1%) was very similar to the prevalence observed when sTfR was used as a sole marker of IDE (47.4%). The prevalence of anemia was 15%. The prevalence of IDA (IDE + hemoglobin < 110 g/L) was higher when accounting for than when disregarding inflammation (8.0% versus 6.2%, P = 0.083). Using sTfR and different SF cutoffs for children with versus without inflammation improved the diagnosis of iron depletion and IDE. Our results confirm that Inuit children are at particularly high risk for iron deficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".