Skeletal muscle-focused guideline development: hierarchical model incorporating muscle form, function, and clinical outcomes
Bibliographic record
Abstract
Sarcopenia, sarcopenic obesity, malnutrition, and cachexia clinical guidelines were created by expert consensus over the past decade. These pathological states all share in common deficits in skeletal muscle mass, and in some cases muscle function, which adversely impact patient outcomes. Early identification is key as some detrimental outcomes are potentially preventable with available treatments. The four guidelines share common design features: patients suspected of having the condition are first screened with a focused clinical history; if positive, the next step is evaluation with either a measure of body "form" (e.g., mass, shape, and composition) or function (e.g., mechanical, endurance, and metabolic); combined form and functional criteria are also recognized. The form and functional "gateway" nodes establish whether or not to proceed with further evaluations and treatments. Intensive discussions among experts focus on selection of these gateway nodes and the final choice is made when consensus is reached. Form and functional measures are often treated as equivalent alternatives when framed in the context of "outcomes" for which they are intended to predict. Here we adapt a classic biological concept stating that "function follows form" to show that pathophysiological links are present between these two different muscle qualities and clinical outcomes. We argue that a hierarchy exists such that outcomes closely follow functions that, in turn, follow form…the OFF rule. The OFF rule explains why functional measures often show stronger associations with outcomes than those quantifying form, helps to frame debates on how to structure the gateway nodes used to identify patients for further evaluation and treatment, and sets out a pathophysiological structure for developing future outcome prediction models.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".