<i>Cribra orbitalia</i> and porotic hyperostosis: A biological approach to diagnosis
Bibliographic record
Abstract
OBJECTIVES: Porotic lesions of the skull (cribra orbitalia and porotic hyperostosis) are one of the most common types of lesion identified in archaeological human bone and have also been found in hominins and non-human primates. Because of the frequency with which such lesions are found there has been extensive debate on the possible causes and whether they are linked, with much of the debate centering on anemia. The biological approach to diagnosis in paleopathology used by Don Ortner and recently proposed more formally as a technique to facilitate diagnosis in paleopathology by Simon Mays may offer a means of answering some of the questions surrounding these lesions. MATERIALS AND METHODS: A review was undertaken of biomedical information on changes in the distribution of marrow type and pattern of conversion of red and mixed marrow, and the potential for re-conversion of yellow marrow with age. The range and type of other conditions that might result in the development of porous lesions were also considered. RESULTS: Combining information from the biomedical literature on marrow type and patterns of conversion with age, with careful evaluation of the type and location of porous lesions in the skull and across the rest of the skeleton will assist in suggesting a diagnosis. DISCUSSION: A wide range of conditions can produce porous lesions in the cranial vault and the orbital roof, but due to anatomical structures and physiological factors such lesions are more likely to occur in the orbital roof. Anemia can produce lesions in both locations, but evidence of marrow expansion is required to confirm it as a cause.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".