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Record W2574286700 · doi:10.1182/blood.v126.23.955.955

Serum Ferritin Is Not Sensitive or Specific for the Diagnosis of Iron Deficiency in Patients with Normocytic Anemia

2015· article· en· W2574286700 on OpenAlexaff
Justin CL Ho, Ivan Stevic, Anthony K.C. Chan, Keith K Lau, Howard H.W. Chan

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of ManitobaMcMaster University
Fundersnot available
KeywordsMedicineFerritinIron deficiencyGastroenterologyAnemiaInternal medicineIron-deficiency anemiaTransferrin saturationConcomitantIron sucrose

Abstract

fetched live from OpenAlex

Introduction: Following the seminal study by Guyatt et al., serum ferritin has been widely accepted as the most accurate surrogate marker for iron deficiency, particularly if ferritin levels are < 45 mg/L. However, as an acute-phase reactant, ferritin levels rise with a number of conditions, including obesity, age, liver disorders, and inflammation. Elevated ferritin levels due to these concomitant clinical conditions may mask the underlying iron deficiency, thus rendering serum ferritin an unreliable marker for iron status. Therefore, the aim of this study is to evaluate the sensitivity and specificity of ferritin for the diagnosis of iron deficiency in patients presenting with normocytic anemia, when response to iron replacement was used as the gold standard for the diagnosis of iron deficiency. Methods: This study is a retrospective case review involving patients referred to an academic hematology clinic from 2003 to 2015 for further evaluation of chronic normocytic anemia without other cell lines abnormalities. Following initial workup to ensure the absence of 1) mixed microcytic-macrocytic anemia, 2) reticulocytosis suggesting acute blood loss or hemolysis, and 3) suboptimally low erythropoietin level, 59 patients received a therapeutic trial of oral ferrous gluconate. Intravenous iron sucrose was provided if patients could not tolerate or were refractory to oral iron therapy. All 59 patients (median age: 71 years, range: 24-93, male:female ratio 23:36) underwent a complete review of records before and after iron therapy for changes in haematological parameters and iron indexes. An increase of Hb ≥ 10.0 g/L from baseline was defined as a response to iron therapy, according to the WHO criteria. Results: The mean pre-treatment ferritin level of the cohort was 110 μg/L (median: 61 μg/L), which was higher than the generally accepted cut-off for iron deficiency. Following iron replacement therapy, the mean ferritin concentration of the cohort was raised to 257 μg/L, thus confirming the efficacy of iron therapy. Overall, 33 patients (56%) responded to iron therapy, experiencing an increase in Hb ≥ 10.0 g/L. Interestingly, 19 (58%) of these 33 patients had a pre-treatment ferritin value > 45 μg/L. Receiver operating characteristic (ROC) analysis of response rates to iron therapy and pre-treatment ferritin levels revealed an area under the curve (AUC) of only 0.492, indicating poor performance of ferritin tests in predicting the response to iron therapy. As such, serum ferritin is inadequate in predicting response to iron therapy in patients presenting with normocytic anemia. Conclusion: Despite the prevailing notion that low ferritin levels are diagnostic of iron deficiency, this retrospective case study exhibited the shortcomings of using ferritin as the sole determinant of iron status. Consequently, patients with normocytic anemia having a normal or high ferritin should not be excluded from a therapeutic trial of iron therapy. Disclosures No relevant conflicts of interest to declare.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.248
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations5
Published2015
Admission routes1
Has abstractyes

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