Effects of metal implants on whole-body dual-energy x-ray absorptiometry measurements of bone mineral content and body composition.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the influence of metal implants on measurements of bone mineral content and body composition by x-ray-based dual-photon absorptiometry. METHODS: Four whole-body dual-photon absorptiometry scans were performed on 13 participants with metal rods either present or absent during the scans. The influence of the amount of metal (50 g, 100 g and 150 g), the proximity of the metal rod to the x-ray source and the reproducibility of any metal-induced effects were evaluated by altering the position or the size of the metal rod used, or both. RESULTS: The presence of metal rods weighing 100 g or 150 g significantly increased reported total body mass and bone mineral content (p < 0.034). Soft-tissue mass was increased when the scan included the 100-g rod (p < 0.003). The proximity of the metal to the x-ray source did not have a significant influence on the body composition changes induced by the metal. The effects of the metal rods on body composition variables were reproducible. CONCLUSION: The presence of metal rods inflated body composition variables measured by dual-photon absorptiometry; however, the effects are reproducible during repeat scans of an individual patient. Metal had the largest impact on whole-body bone mineral content, causing errors of 1.5%-3%.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".