Effects of osteoarthritis on age‐at‐death estimates from the human pelvis
Bibliographic record
Abstract
OBJECTIVES: This study examined the degree of error in age-at-death estimates when osteoarthritis (OA) is present in three separate pelvic joint areas: (1) the pubic symphysis, (2) the auricular surface, and (3) the acetabulum from a modern known-age European cemetery sample of adults. MATERIALS AND METHODS: Age-at-death ranged from 17 to 79 years (x̄ =50.9 years; n = 252). OA in the pelvic joints was evaluated using standard ranked categorical scoring. Composite OA scores were derived through principal component analysis. Blind age assessments and all analyses were performed separately by region. Error between adult age groups (young, middle, old) and between OA severity groups (low, middle, high) was evaluated using one-way ANOVAs with post-hoc testing, ordinary least squares regression, and transition analysis with a cumulative probit model. Ages-at-transition were compared with Nphases2. RESULTS: Three significant results emerge. First, OA severity has an effect on the accuracy of age estimates from os coxa joints in this sample. Second, this influence is most significant for different age cohorts in each joint region, demonstrating that varied rates of arthritic trait progression occur between the auricular surface, pubic symphyses, and acetabulum. Third, those with OA appear to be aging faster, a consistent trend among the os coxa regions. CONCLUSIONS: These results have significant consequences for understanding the rate of bone remodeling in relation to disease, aging, and the evaluation of skeletal age indicators.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".