Pathways for integration of body composition and energy metabolism assessments throughout the nutrition care process: Protocol for implementation planning
Bibliographic record
Abstract
BACKGROUND & AIMS: Body composition and energy metabolism (BC and EM) assessments inform patients' nutritional status and allow the identification, diagnosis, and management of core nutritional problems. However, a structured approach or care model for integrating these assessments into the nutrition care process is currently lacking. This manuscript describes the protocol for a planning project aimed at convening a multidisciplinary team of researchers, healthcare providers, provincial managers, and industry partners to identify a practical pathway for implementing BC methods and indirect calorimetry for EM assessment in targeted clinical settings. METHODS: Using purposive sampling, dietitians from six specialty areas (oncology, liver or kidney diseases, critical care, weight management/bariatric surgery, and inpatient care) within Alberta Health Services are invited to participate in this two-phase planning project. In phase 1, participants attend a 4-h evidence-based educational session to establish a baseline understanding of BC and EM. In phase 2, seven 4-h focus group discussions are conducted, facilitated by trained moderators using a semi-structured interview guide. Discussions are audio-recorded and will be transcribed verbatim. Data will be analyzed using an inductive, descriptive qualitative content analysis approach. The Consolidated Framework for Implementation Research will inform barriers and facilitators identification, and the Expert Recommendations for Implementing Change will inform implementations strategies. Peer debriefings and reflexivity will ensure trustworthiness of the findings. CONCLUSIONS: The proposed pathway will serve as a roadmap to dietitians and other healthcare professionals involved in nutrition assessment and a catalyst for practice improvement. Beyond outlining the proposed pathway, this initiative will inform the development of a larger implementation project focused on evaluating the feasibility and cost-effectiveness of BC and EM assessments in daily practice. Ultimately, a structured approach to integrating these assessments into routine care may enhance patient outcomes and improve nutrition care standards.
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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 | 0.140 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.072 | 0.017 |
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".