Why should women have lower reference limits for haemoglobin and ferritin concentrations than men?
Bibliographic record
Abstract
The need to transport oxygen and remove carbon dioxide from animal tissue is a fundamental requirement of life, independent of age or sex.1 The role of iron in humans and many other mammals is central to this process. 2 3 Haemoglobin concentration and red blood cell count are important diagnostic indicators for anaemia in humans and animals. In prepubertal humans no major differences can be found between the sexes in red blood cell count or haemoglobin and serum ferritin concentrations.4 Only after the onset of menstruation does a difference emerge.4 Not until 10 years after the menopause does this situation revert in women, when the haemoglobin concentration becomes similar to that of aged matched men. 4 5 This situation is compounded by the fact that modern women have a different reproductive history from those in the past. They reach sexual maturity at an earlier age, have fewer pregnancies, and breast feed for shorter periods; as such they menstruate for more years than women in the past. Menstruation is the principal cause of iron loss in women.6–8 Furthermore, 90% of UK females of childbearing age do not achieve the recommended daily intake of elemental iron (14.8 mg) from their diet.9 Evaluation of the haemoglobin concentration and red blood cell count of women from Canada, Central America, China, and the United States shows that this situation is widespread. 4 10–14 Women worldwide are at risk of being in a negative iron balance, and by current criteria if their haemoglobin concentration is less than 115 g/l they are deemed to be anaemic, whereas in men the cut-off point is 130 g/l.15 As far as the authors are aware, of the primates only humans show a sex difference in haemoglobin concentration and red blood …
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| 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".