NEW DIMENSIONS OF ANEMIA IN PALEOPATHOLOGY: DEVELOPMENT AND APPLICATION OF NOVEL METHODS FOR ANEMIA DIAGNOSIS IN SKELETAL REMAINS
Bibliographic record
Abstract
In modern populations, anemia is highly prevalent and can have a significant effect on health at the individual and population level. The condition is likely to have been as important for past communities, but research on the subject is challenging due to the limitations of paleopathological anemia diagnosis, which typically relies on assessment of porosity. This research explores the utility of quantitative methods for anemia diagnosis in skeletal remains, and uses these novel methods to investigate the co-occurrence of anemia and scurvy in three communities from 18th-19th century Quebec. To investigate metric changes associated with anemia, hematological and sternal imaging data from a modern cohort of individuals was assessed for changes associated with anemia/marrow hyperplasia. Additionally, sixty-eight orbits from archaeological individuals underwent micro-CT analysis, and were also evaluated for metric differences related to marrow hyperplasia. Results demonstrated that there are changes in bone microarchitecture associated with anemia, and that these changes are identifiable through visual and metric assessment. Based on these principles, a framework for anemia diagnosis was developed, and used to explore how anemia may have interacted with scurvy to affect children in Colonial Quebec. Prevalence of both conditions was high, and patterns of metabolic disease at urban and rural sites were similar, suggesting that children across different sites were at risk for developing scurvy and anemia. This research highlights the importance of looking beyond porosity for anemia assessment in skeletal remains. It demonstrates the utility of visualizing and assessing the internal marrow space for anemia assessment, and demonstrates that metric data has a place in analyses of anemia in archaeological contexts. It also shows that investigating co-occurrence and clustering can be a valuable source of information on past health, and demonstrates the utility of methods that allow us to do so.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".