The causes of porotic hyperostosis and cribra orbitalia: A reappraisal of the iron‐deficiency‐anemia hypothesis
Bibliographic record
Abstract
Porosities in the outer table of the cranial vault (porotic hyperostosis) and orbital roof (cribra orbitalia) are among the most frequent pathological lesions seen in ancient human skeletal collections. Since the 1950s, chronic iron-deficiency anemia has been widely accepted as the probable cause of both conditions. Based on this proposed etiology, bioarchaeologists use the prevalence of these conditions to infer living conditions conducive to dietary iron deficiency, iron malabsorption, and iron loss from both diarrheal disease and intestinal parasites in earlier human populations. This iron-deficiency-anemia hypothesis is inconsistent with recent hematological research that shows iron deficiency per se cannot sustain the massive red blood cell production that causes the marrow expansion responsible for these lesions. Several lines of evidence suggest that the accelerated loss and compensatory over-production of red blood cells seen in hemolytic and megaloblastic anemias is the most likely proximate cause of porotic hyperostosis. Although cranial vault and orbital roof porosities are sometimes conflated under the term porotic hyperostosis, paleopathological and clinical evidence suggests they often have different etiologies. Reconsidering the etiology of these skeletal conditions has important implications for current interpretations of malnutrition and infectious disease in earlier human populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".