Skeletal manifestations of anemia in the sternum in a modern clinical sample: An initial investigation
Bibliographic record
Abstract
Anemia is a globally significant condition, both today and throughout history. Studying how it affected past communities contributes to understanding its impact on vulnerable groups. However, anemia diagnosis in skeletal remains is challenging, and improving methods for diagnosis is necessary for moving forward. In current clinical practice, sternal bone marrow undergoes anemia-related changes, which could affect underlying bone structure, but the sternum has never been investigated as a skeletal marker of anemia. We used a cohort of individuals with known hematological data (N = 23) to investigate whether there are quantitative bone changes in the sternum of individuals clinically diagnosed with anemia. Sternal bone features were measured using CT imaging to calculate a ratio of the relative cortical bone to trabecular space sample. The sample was separated into non-anemic and anemic cohorts based on World Health Organization diagnostic parameters. We found significant differences in ratios between those with and without anemia, suggesting that sternal bone ratio measurements could be used as a diagnostic parameter. In individuals without clinically-diagnosed anemia, ratios never exceeded 3.6 in the manubrium and 2.3 in the sternal body. We repeated these ratio measurements using micro-CT analysis on a sample of archaeological sternums and found that the ratio diagnostic parameter could also be used in this sample to identify those who could have experienced anemia. These results demonstrate the utility of quantitative methods for diagnosing anemia in skeletal remains and suggest that sternal ratio measurements can be used as part of anemia diagnosis in past contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".