Assessing diagnostic certainty for scurvy and rickets in human skeletal remains
Bibliographic record
Abstract
OBJECTIVES: Identifying scurvy and rickets has important implications for understanding adaptations and variability among past communities, and bioarchaeologists now regularly evaluate these conditions. Due to the increased number of studies, cases with less clear-cut lesions and variable preservation are now frequently reported. Despite an improved understanding of the biological mechanisms for disease expression, there is a lack of consensus on the language used to express diagnostic certainty, limiting comparability. This article aims to address these issues and provide recommendations on more consistent diagnostic terminology using widely accepted diagnostic methodology based on biological mechanisms. MATERIALS AND METHODS: We review diagnostic terms used in bioarchaeology by considering published cases of rickets, scurvy and co-occurrence alongside M.B.B.'s past project notes. We also consider differences in the diagnosis of rickets and scurvy in living and archeological individuals. RESULTS: We provide recommendations on a framework that can be used to show diagnostic certainty in cases of rickets, scurvy, and co-occurrence. Core lesions of rickets and scurvy are used alongside a limited lexicon of diagnostic terminology based on the Istanbul protocol. DISCUSSION: It is not the number of lesions that determines whether an individual is assigned to a particular diagnosis category, but rather the range and expression of lesions present. Avoiding a "tick-list" approach to core lesions of these diseases will be critical to ensure that identifying rickets and scurvy continues to contribute to understanding adaptations and variability among past communities. The framework allows more consistency in diagnostic certainty, facilitating greater comparability in research.
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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 | 0.020 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".