Evaluating Hemoglobin Thresholds on the Basis of Sex: Preliminary Insights from a Systematic Review
Bibliographic record
Abstract
Introduction: World Health Organization hemoglobin thresholds to define anemia are identical for males and females until menarche after which they diverge to 130 g/L and 120 g/L, respectively. There is increasing concern that sex-defined hemoglobin lower limits of normal (LLN) are predicated on sampling females with untreated iron deficiency given its extremely high prevalence and relative under-prioritization in medicine. Inclusion of women with iron deficiency would, in and of itself, result in inappropriately low LLN. We therefore conducted a systematic review to evaluate the published literature to date on sex-based hemoglobin reference intervals (RIs). Methods: A systematic review was conducted using a comprehensive search of MEDLINE, EMBASE, CINAHL and Web of Science from inception to November 2022. Two reviewers independently screened and performed data extraction. A third reviewer adjudicated decisions when a consensus could not be reached. We excluded studies that only evaluated hemoglobin RIs in a state of know disease (e.g. untreated iron deficiency, bone marrow pathology, inherited red blood cell disorders, chronic systemic diseases). A priori defined quality assessment of all manuscripts was performed and included the following criteria: the manner in which “health” was defined in the sampled population, whether there was adherence to Clinical and Laboratory Standards (CLSI) guidelines for RI establishment studies, methodological rigour applied and transparent reporting of results. Results: 6411 articles were identified for title/abstract review. 885 articles were selected for full-text analysis, of which 143 extractions have been completed. We report the results of this convenience sample of the first 143 articles. Sixty-two articles aimed to establish hemoglobin RIs or thresholds to define anemia. The remainder of the studies (n=81) were establishing a hemoglobin mean. 32/143 articles evaluated a female population only, 9/143 were male-only and 102/143 presented both. Of those that sought to establish a RI, 42/62 (67.8%) adhered to CLSI guidelines. 110/143 articles included people with known iron deficiency or were at risk of iron deficiency. Of the 82 articles that considered iron status in the study design, 57 used ferritin as the screening laboratory parameter with variation in the definition of a normal ferritin to exclude iron deficiency (21 articles used a ferritin LLN of 12.0 ug/L,15 used a LLN of 16.0 ug/L, 7 used a LLN between 16.0-30.0 ug/L, and 1 used a LLN of 50 ug/L). Conclusion: 77% of the sample of studies evaluated failed to explicitly exclude individuals with known iron deficiency or its risk factors when deriving their LLN. Moreover, in studies that considered iron status of the sampled population, the majority used a ferritin LLN below conventional standards to define iron deficiency. Based on our preliminary findings, we suspect we will identify an overwhelming lack of consideration of the potential for iron deficiency to be present in patients used to define the LLN despite its well-established impact on erythropoiesis and its extremely high prevalence, particularly in females of reproductive age. At the annual meeting we will present more complete results, including a further exploration of the impact of excluding individuals with or at risk of iron deficiency from the cohort used to establish the LLN.
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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 | 0.043 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".