The Australian and New Zealand Society for Sarcopenia and Frailty Research (<scp>ANZSSFR</scp>) sarcopenia diagnosis and management task force: Findings from the consumer expert Delphi process
Bibliographic record
Abstract
OBJECTIVES: To develop guidelines, informed by health-care consumer values and preferences, for sarcopenia prevention, assessment and management for use by clinicians and researchers in Australia and New Zealand. METHODS: A three-phase Consumer Expert Delphi process was undertaken between July 2020 and August 2021. Consumer experts included adults with lived experience of sarcopenia or health-care utilisation. Phase 1 involved a structured meeting of the Australian and New Zealand Society for Sarcopenia and Frailty Research (ANZSSFR) Sarcopenia Diagnosis and Management Task Force and consumer representatives from which the Phase 2 survey was developed. In Phase 2, consumers from Australia and New Zealand were surveyed online with opinions sought on sarcopenia outcome priorities, consultation preferences and interventions. Findings were confirmed and disseminated in Phase 3. Descriptive statistical analyses were performed. RESULTS: Twenty-four consumers (mean ± standard deviation age 67.5 ± 12.8 years, 18 women) participated in Phase 2. Ten (42%) identified as being interested in sarcopenia, 7 (29%) were health-care consumers and 6 (25%) self-reported having/believing they have sarcopenia. Consumers identified physical performance, living circumstances, morale, quality of life and social connectedness as the most important outcomes related to sarcopenia. Consumers either had no preference (46%) or preferred their doctor (40%) to diagnose sarcopenia and preferred to undergo assessments at least yearly (54%). For prevention and treatment, 46% of consumers preferred resistance exercise, 2-3 times per week (54%). CONCLUSIONS: Consumer preferences reported in this study can inform the implementation of sarcopenia guidelines into clinical practice at local, state and national levels across Australia and New Zealand.
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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 | 0.121 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".