Below the Callus Surface: Applying Paleohistological Techniques to Understand the Biology of Bone Healing in Skeletonized Human Remains
Bibliographic record
Abstract
OBJECTIVES: Bone trauma is a common occurrence in human skeletal remains. Macroscopic and imaging scrutiny is the approach most currently used to analyze and describe trauma. Nevertheless, this line of inquiry may not be sufficient to accurately identify the type of traumatic lesion and the associated degree of bone healing. To test the usefulness of histology in the examination of bone healing biology, we used an integrative approach that combines gross inspection and microscopy. MATERIALS AND METHODS: Six bone samples belonging to 5 adult individuals with signs of bone trauma were collected from the Human Identified Skeletal Collection from the Museu Bocage (Lisbon, Portugal). Previous to sampling, the lesions were described according to their location, morphology, and healing status. After sampling, the bone specimens were prepared for plane light and polarized light analysis. RESULTS: The histological analysis was pivotal: (1) to differentiate between types of traumatic lesions; (2) to ascertain the posttraumatic interval, and (3) to diagnose other associated pathological conditions. CONCLUSION: The outer surface of a bone lesion may not give a complete picture of the biology of the tissue's response. Accordingly, microscopic analysis is essential to differentiate, characterize, and classify trauma signs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".