Highlights from the 2019 International Congress on Frailty and Sarcopenia Research
Bibliographic record
Abstract
The International Conference of Frailty and Sarcopenia Research was held in Miami, Florida, in February of 2019 (1). The 9th edition of this conference had the highest attendance rate since its debut and is now a major venue that attracts the best key opinion leaders in the field of sarcopenia and frailty. Approximately 400- 500 delegates from around the world shared their results across the spectrum of research, from animal studies targeting novel mechanisms to large clinical trials for improving function, reducing disability, and improving quality of life in aging persons. With the recent implementation of ICD-10 code for sarcopenia (2), more and more industry leaders are looking for novel strategies and treatments counteracting the decline of skeletal muscle and physical function. By identifying sarcopenia as a disease (2) and the increasing number of older adults in the world (3), targeting and understanding the process of sarcopenia will be essential for geriatricians and gerontologists in the coming years. This paper will present the major highlights of this conference.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".