Assessment of body composition in spinal cord injury: A scoping review
Bibliographic record
Abstract
The objective of this scoping review was to map the evidence on measurement properties of body composition tools to assess whole-body and regional fat and fat-free mass in adults with SCI, and to identify research gaps in order to set future research priorities. Electronic databases of PubMed, EMBASE and the Cochrane library were searched up to April 2020. Included studies employed assessments related to whole-body or regional fat and/or fat-free mass and provided data to quantify measurement properties that involved adults with SCI. All searches and data extractions were conducted by two independent reviewers. The scoping review was designed and conducted together with an expert panel (n = 8) that represented research, clinical, nutritional and lived SCI experience. The panel collaboratively determined the scope and design of the review and interpreted its findings. Additionally, the expert panel reached out to their professional networks to gain further stakeholder feedback via interactive practitioner surveys and workshops with people with SCI. The research gaps identified by the review, together with discussions among the expert panel including consideration of the survey and workshop feedback, informed the formulation of future research priorities. A total of 42 eligible articles were identified (1,011 males and 143 females). The only tool supported by studies showing both acceptable test-retest reliability and convergent validity was whole-body dual-energy x-ray absorptiometry (DXA). The survey/workshop participants considered the measurement burden of DXA acceptable as long as it was reliable, valid and would do no harm (e.g. radiation, skin damage). Practitioners considered cost and accessibility of DXA major barriers in applied settings. The survey/workshop participants expressed a preference towards simple tools if they could be confident in their reliability and validity. This review suggests that future research should prioritize reliability and validity studies on: (1) DXA as a surrogate 'gold standard' tool to assess whole-body composition, regional fat and fat-free mass; and (2) skinfold thickness and waist circumference as practical low-cost tools to assess regional fat mass in persons with SCI, and (3) females to explore potential sex differences of body composition assessment tools. Registration review protocol: CRD42018090187 (PROSPERO).
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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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".