Explanatory sequential mixed‐methods approach to understand how registered dietitians implemented computed tomography skeletal muscle assessments in clinical practice
Bibliographic record
Abstract
BACKGROUND: There is a need to adopt valid techniques to assess skeletal muscle (SM) in clinical practice. SM can be precisely quantified from computed tomography (CT) images. This study describes how registered dietitians (RDs), trained to quantify SM from CT images, implemented this technique in clinical practice. METHODS: This was an explanatory sequential mixed-methods design with a quantitative and a qualitative phase. RDs collected data describing how they implemented CT SM assessments in clinical practice, followed by a focus group exploring barriers and enablers to using CT SM assessments. RESULTS: RDs (N = 4) completed 96 CT SM assessments, with most (94%, N = 90/96) taking <15 min to complete. RDs identified reduced muscle mass in 63% (N = 45/72) of men and 71% (N = 17/24) of women. RDs used results of CT SM assessments to increase protein composition of the diet/nutrition support, advocate for initiation or longer duration of nutrition support, coordinate nutrition care, and provide nutrition education to patients and other health service providers. The main barriers to implementing CT SM assessments in clinical practice related to cumbersome health system processes (ie, CT image acquisition) and challenges integrating CT image analysis software into the health system computing environment. CONCLUSION: Preliminary results suggest RDs found CT SM assessments positively contributed to their nutrition care practice, particularly in completing nutrition assessments and in planning, advocating for, and implementing nutrition interventions. Use of CT SM assessments in clinical practice requires innovative IT solutions and strategies to support skill development and use in clinical nutrition care.
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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 | 0.086 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".