Developing a UK sarcopenia registry: recruitment and baseline characteristics of the SarcNet pilot
Bibliographic record
Abstract
BACKGROUND: sarcopenia registries are a potential method to meet the challenge of recruitment to sarcopenia trials. We tested the feasibility of setting up a UK sarcopenia registry, the feasibility of recruitment methods and sought to characterise the pilot registry population. METHODS: six diverse UK sites took part, with potential participants aged 65 and over approached via mailshots from local primary care practices. Telephone pre-screening using the SARC-F score was followed by in-person screening and baseline visit. Co-morbidities, medications, grip strength, Short Physical Performance Battery, bioimpedance analysis, Geriatric Depression Score, Montreal Cognitive Assessment, Sarcopenia Quality of Life score were performed and permission sought for future recontact. Descriptive statistics for recruitment rates and baseline measures were generated; an embedded randomised trial examined the effect of a University logo on the primary care mailshot on recruitment rates. RESULTS: sixteen practices contributed a total of 3,508 letters. In total, 428 replies were received (12% response rate); 380 underwent telephone pre-screening of whom 215 (57%) were eligible to attend a screening visit; 150 participants were recruited (40% of those pre-screened) with 147 contributing baseline data. No significant difference was seen in response rates between mailshots with and without the logo (between-group difference 1.1% [95% confidence interval -1.0% to 3.4%], P = 0.31). The mean age of enrollees was 78 years; 72 (49%) were women. In total, 138/147 (94%) had probable sarcopenia on European Working Group on Sarcopenia 2019 criteria and 145/147 (98%) agreed to be recontacted about future studies. CONCLUSION: recruitment to a multisite UK sarcopenia registry is feasible, with high levels of consent for recontact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".