The third dimension in palaeopathology: How can three‐dimensional imaging by computed tomography bring an added value to retrospective diagnosis?
Bibliographic record
Abstract
Abstract Three‐dimensional (3D) imaging is now extensively used for studying ancient human and animal bones. This method has been consensually adopted by palaeoanthropologists, but its interest in palaeopathology has been challenged. The aim of this paper is to illustrate the contribution of 3D reconstructions to retrospective diagnosis in palaeopathology. We selected six palaeopathological cases among our research corpus representing three nosographic categories (trauma, infection and neoplasia) from various periods ranging from the Middle Palaeolithic to the beginning of the Modern Era. For each case, we compared the diagnostic value of plain X‐ray, computed tomography (CT) slices, and 3D reconstructions. The latter were performed using TIVMI program, a free software for research use developed by one of us. Reconstructions are obtained by surface extraction that follows a segmentation process. We showed that this 3D method allowed reconstructing/quantifying pathological processes on ancient bones, usefully supplementing conventional radiological analyses and clearly bringing an added value to retrospective diagnosis in palaeopathology.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".