Pediatric paleoradiology: Applications and best practice protocols for image acquisition and reporting
Bibliographic record
Abstract
Abstract This paper aggregates and summarizes studies that use paleoradiology techniques in the analysis of pediatric remains. Building on these findings, we offer evidence‐based, best‐practice protocols for the use of radiographic techniques in the assessment of pediatric remains from archaeological contexts. We also provide a recommended reporting worksheet for recording findings in a standardized way. After reviewing studies of pediatric remains that used radiographic techniques, with a focus on three major bioarchaeological journals, PubMed, and the Yearbook of Mummy Studies, we identified areas for improvement in the acquisition and reporting of paleoradiographic findings in pediatric remains. Although radiological techniques are used with increasing frequency in the assessment of pediatric remains, no standardized pediatric imaging protocols presently exist. We combine insights gained from existing literature with current clinical imaging protocols in order to provide best‐practice protocols for the acquisition and reporting of radiographic findings from archeological contexts, specific to pediatric remains. This is, as far as we know, the first proposal of imaging protocols specifically tailored for the assessment of pediatric remains from bioarchaeological contexts. Recognizing the limitations faced by archaeologists around the use of imaging technologies in their fieldwork (including lack of funding, equipment access, and permit acquisition, among others), this review is offered not to criticize previous work, but in the hope that the need for appropriate radiographic study can be incorporated into research plans and funding applications from the beginning, and that granting agencies will encourage and support these types of analyses in skeletal studies. It holds implications for how paleoradiology techniques might be systematically used and applied to pediatric remains from multiple time periods, across the world. Our proposed imaging acquisition and reporting protocols are primarily based on English‐language sources. Further assessment of the proposed protocols, following systematic application in multiple contexts, is recommended.
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 | 0.175 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".