CT imaging of human mummies: a critical review of the literature (1979–2005)
Bibliographic record
Abstract
Abstract The number of computerised tomography (CT) investigations of mummies has increased since the first published study in 1979. However, this approach has never been validated. We present a critical analysis of the literature (1979 to 2005). Relevant articles were selected via a MedLine search and analysed according to CT technique, methodology, and author's speciality. Thirty‐one original articles matched our selection criteria. Of these studies, 42% were authored by radiologists, while 26% had no contribution from radiologists. Hypothesis‐driven papers comprised only 9.7% of the total. While 84% of the studies had a stated purpose for conducting the CT study, only 67% of studies defined their CT protocol clearly. CT was used to study mummification techniques in 74% of instances, and/or to detect disease in 58%. Conclusions based on CT analysis were derived in 84% of studies, but only 32% of these answered specific questions. Furthermore, only 36% of these conclusions were related to the stated purpose of the study. Using the criteria of the grading system we developed, we found that 61% of studies were supported only by weak evidence. We conclude that evidence‐based research with better design should be encouraged in future palaeoradiological studies. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".