Enthesopathies as occupational stress markers: Evidence from the upper limb
Bibliographic record
Abstract
Enthesopathies--that is, "musculo-skeletal stress markers"--are frequently used to reconstruct past lifestyles and activity patterns. Relatively little attention has been paid in physical anthropology to methodological gaps implicit in this approach: almost all methods previously employed neglect current medical insights into enthesopathies and the distinction between healthy and pathological aspects has been arbitrary. This study presents a new visual method of studying fibrocartilaginous enthesopathies of the upper limb (modified from Villotte: Bull Mém Soc Anthropol Paris n.s. 18 (2006) 65-85), and application of this method to 367 males who died between the 18th and 20th centuries, from four European identified skeletal collections: the Christ Church Spitalfields Collection, the identified skeletal collection of the anthropological museum of the University of Coimbra, and the Sassari and Bologna collections of the museum of Anthropology, University of Bologna. The analysis, using generalized estimating equations to model repeated binary outcome variables, has established a strong link between enthesopathies and physical activity: men with occupations involving heavy manual tasks have significantly (P-value < 0.001) more lesions of the upper limbs than nonmanual and light manual workers. Probability of the presence of an enthesopathy also increases with age and is higher for the right side compared with the left. Our study failed to distinguish significant differences between the collections when adjusted for the other effects. It appears that enthesopathies can be used to reconstruct past lifestyles of populations if physical anthropologists: 1) pay attention to the choice of entheses in their studies and 2) use appropriate methods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".