Proceedings of the Canadian Frailty Network Workshop: Identifying Biomarkers of Frailty to Support Frailty Risk Assessment, Diagnosis and Prognosis. Toronto, January 15, 2018
Bibliographic record
Abstract
The Canadian Frailty Network (CFN), a pan-Canadian not-for-profit organization funded by the Government of Canada through the Networks of Centres of Excellence Program, is dedicated to improving the care of older Canadians living with frailty. The CFN has partnered with the Canadian Longitudinal Study on Aging (CLSA) to measure potential frailty biomarkers in biological samples (whole blood, plasma, urine) collected in over 30,000 CLSA participants. CFN hosted a workshop in Toronto on January 15 2018, bringing together experts in the field of biomarkers, aging and frailty. The overall objectives of the workshop were to start building a consensus on potential frailty biomarker domains and identify specific frailty biomarkers to be measured in the CLSA biological samples. The workshop was structured with presentations in the morning to frame the discussions for the afternoon session, which was organized as a free-flowing discussion to benefit from the expertise of the participants. Participants and speakers were from Canada, Italy, Spain, United Kingdom and the United States. Herein we provide pertinent background information, a summary of all the presentations with key figures and tables, and the distillation of the discussions. In addition, moving forward, the principles CFN will use to approach frailty biomarker research and development are outlined. Findings from the workshop are helping CFN and CLSA plan and conduct the analysis of biomarkers in the CLSA samples and which will inform a follow-up data access competition.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".