The changing shape of palaeopathology: The contribution of skeletal shape analyses to investigations of pathological conditions
Bibliographic record
Abstract
Abstract Analyses of human skeletal shape and geometry are used to investigate questions related to habitual activities and physical lifeways, as well as biological distance and relatedness. Recently, these methods have been applied to research concerning human evolutionary predisposition for disease, as well as functional experiences of pathological conditions. The use of these methods to address palaeopathological questions are relatively new, but related questions and approaches are gaining momentum. This manuscript provides an in‐depth review of the current state of this palaeopathological research by undertaking a meta‐analysis of anthropological literature. From the results of the meta‐analysis, we observe an increase in the use of quantitative shape analyses in palaeopathology, and identify four key themes in this literature: (1) description and diagnosis, (2) shapes that increase pathological risk, (3) shape change that arises from pathology, and (4) shape used for social insight. As this area of study develops, we recommend adaptations to measurement and data collection; comparative examinations of remains at the individual, population, and species levels; and, when possible, representation of all human variation through the inclusion of pathological individuals in geometric analyses. Palaeopathologists are ideally suited to investigate the relationship between bone shape and health, which may prove essential to the continued understanding of disease in both past and contemporary 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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".