Revising Ferritin Lower Limits: It’s Time to Raise the Bar on Iron Deficiency
Bibliographic record
Abstract
Ferritin is a key diagnostic marker of iron deficiency (ID), but the interpretative guidance provided to physicians varies significantly. Clear discrepancies exist between clinical guidelines that recommend evidence-based ferritin cutoffs and clinical laboratories that report highly variable ferritin reference intervals (RIs) derived from apparently healthy populations. In this study, clinical laboratories across North America were surveyed to assess the RIs provided with ferritin results. Although clinical guidelines often recommend ferritin cutoffs of 15 or 30 µg/L to identify uncomplicated ID, the survey showed that 18 of 23 responding laboratories reported female RI lower limits well below 15 µg/L. To understand the clinical impact, we analyzed 52 027 unique patient ferritin values over a 5-year period (2013-2017) from a tertiary care hospital. In this population, the 90th percentile ferritin cutoff to identify ID anemia in adults was 24 µg/L in female patients and 25 µg/L in male patients. Distribution of ferritin results in female patients showed that menopausal status had a significant effect on median values, which increased 2- to 3-fold in the postmenopausal state. Furthermore, sorting the data for female patients by physician specialty showed the highest prevalence of low ferritin values in patients seen in obstetrics and gynecology. This study highlights the discrepancy between clinical guidelines and clinical laboratory practice for ferritin reporting and indicates that ferritin RIs, particularly for female patients, are set to an inappropriately low threshold in most clinical laboratories in North America; this level provides good specificity but poor sensitivity when screening for ID.
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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 | 0.046 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".