A Framework for Anemia Differential Diagnosis in Paleopathology Incorporating Metric Methods
Bibliographic record
Abstract
OBJECTIVES: This paper explores metric manifestations of anemia in crania undergoing growth and development using micro-CT imaging. It proposes a framework for assigning a most-likely diagnostic option for anemia, based on evaluating the parameters proposed in this study. MATERIALS AND METHODS: Sixty-eight orbits/frontal bones of individuals aged birth to 15 years from Quebecois and Dutch archaeological collections dating to the 18th and 19th centuries underwent micro-CT analysis. Individuals were visually assessed for skeletal manifestations of marrow hyperplasia within the internal marrow space using a scoring rubric. Bone microarchitecture measurements were used to calculate T-scores and identify individuals who displayed potential manifestations of marrow hyperplasia. Relative cortical thickness ratios of the frontal bone were calculated for 16 individuals. Error testing was performed for all evaluations. RESULTS: Using the micro-CT analysis and our diagnostic framework, anemia was inferred in 16% (10/61) of the sample that was preserved well enough for the study. Trabecular separation T-scores were considered the most significant metric for evaluating anemia. Frontal bone ratios were regarded as less insightful due to the imaging technique used. Age had a significant effect on bone measurements, and high repeatability was seen across methods. DISCUSSION: In this study, recommendations for assigning a diagnostic option prioritize evaluating metric features strongly related to anemia through a biological approach that considers the etiology of marrow hyperplasia. Including a combination of metric and internal visual evaluation criteria provides clearer lines of evidence for the assessment of abnormal bone changes associated with anemia beyond the macroscopic evaluation of porous lesions.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".