Use of dual X-ray absorptiometry to measure body mass during short- to medium-term trials of nutrition and exercise interventions
Bibliographic record
Abstract
Dual X-ray absorptiometry (DXA) has a range of clinical applications, from assessing associations between adipose or lean body mass and the risk of disease to measuring the effects of dietary interventions on adipose deposition and oxidation and/or muscle accumulation. Many lifestyle-related studies, however, are short- to medium-term interventions, and inter- or intradevice variation between DXA scanners can facilitate type I and type II errors during data analysis. Studies demonstrate that variation in body composition measurements exist not only between DXA instruments using fan-beam and pencil-beam technologies but also between DXA instruments produced by different manufacturers. Moreover, studies show inter- and intrainstrument variation between identical DXA instruments. Such inter- and intrascan variability between instruments can be compounded by the particular patient population being investigated. The objective of this review is to discuss inter- and intradevice variation of DXA instruments and to outline quality control procedures that should be implemented prior to initiating short-term single or multicenter clinical trials that use DXA to investigate the effects of an intervention on loss or accretion of lean or fat mass.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".