Current and emerging histomorphometric and imaging techniques for assessing<scp>age‐at‐death</scp>and cortical bone quality
Bibliographic record
Abstract
Abstract Bones are dynamic living organs that undergo continual change throughout life. An internal process of tissue renewal, called remodeling, removes mature microscopic packets of bone, and replaces them with new bone in a highly coordinated manner. To date, it remains difficult to directly observe and track individual remodeling events in cortical bone due to the small size of the structures involved. High‐resolution imaging techniques hold the potential to provide novel three‐dimensional information pertaining to changes in bone's microarchitecture, cortical porosity, and the remodeling process. This review critically explores the methodological approaches used historically by researchers to assess the products of remodeling within cortical bone and relate it to age‐at‐death estimation, extending from histology to modern ex vivo imaging modalities, and discusses the growing potential of in vivo imaging. We further provide an introduction to various histological indicators of bone quality and fragility, their forensic relevance, and examples of novel imaging modalities employed for their investigation. The review concludes with an introduction to cutting‐edge in vivo four‐dimensional imaging techniques that include the use of animal models to shed new light on the dynamic nature of bone, and the processes of bone aging and disease. Data gleaned from these new insights will ultimately lead to the development of future histologic age‐estimation methods in forensic anthropology. This article is categorized under: Forensic Anthropology > Age Assessment Forensic Chemistry and Trace Evidence > Emerging Technologies and Methods Forensic Medicine > Imaging Modalities
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".