Piloting a training program in computed tomography skeletal muscle assessment for registered dietitians
Bibliographic record
Abstract
Abstract Background Consensus definitions for disease‐associated malnutrition and sarcopenia include reduced skeletal muscle mass as a diagnostic criterion. There is a need to develop and validate techniques to assess skeletal muscle in clinical practice. Skeletal muscle mass can be precisely quantified from computed tomography (CT) images. This pilot study aimed to train registered dietitians (RDs) to complete precise skeletal muscle measurements using CT. Methods Purposive sampling identified RDs employed in clinical areas in which CT scans are routinely performed. CT training included (1) a 3‐Day training session focused on manual segmentation of skeletal muscle cross‐sectional areas (cm2, centimeter squared) from abdominal CT images at the third lumbar vertebra (L3), using sliceOmatic® software, and (2) a precision assessment to quantify the intraobserver and interobserver precision error of repeated skeletal muscle measurements (30 images in duplicate). Precision error is reported as the root mean standard deviation (cm2) and percent coefficient of variation (%CV), our primary performance indicator, was defined as a precision error of <2%. Results Five RDs completed CT training. RDs were from three clinical areas: cancer care (N = 1), surgery (N = 2), and critical care (N = 1). RDs' precision error was low and below the minimal acceptable error of <2%; intraobserver error was ≤1.8 cm2 (range, 0.8–1.8 cm2) or ≤1.5% (range, 0.8%–1.5%) and interobserver error was 1.2 cm2 or 1.1%. Conclusion RDs can be trained to perform precise CT skeletal muscle measurements. Increasing capacity to assess skeletal muscle is a first step toward developing this technique for use in clinical practice.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".