INTERSECTION OF AGE, SEX, AND FRAILTY: CONSIDERATIONS FOR TRANSLATIONAL GEROSCIENCE, GERIATRICS, AND FORTITUDE
Bibliographic record
Abstract
Abstract Understanding aging, optimising care of older adults and facilitating fortitude, must consider the intersection of chronological age, sex and frailty. While existing initiatives consider these factors separately, they occur together in old age and have complex interactions. This symposium brings together basic and clinical scientists, at differing career stages, from three countries, who have explored these issues in their research. Dr Alice Kane will discuss work from her laboratory in the Institute for Systems Biology, USA, on important sex differences in translational biomarkers for ageing and frailty. Dr John Mach will describe insights from a pre-clinical model of chronic drug therapy and polypharmacy, developed at the Kolling Institute, Australia, on the impact of age, sex and frailty on drug effects. Prof Sarah Hilmer, Australia, will discuss key considerations in age, sex and frailty for development of drugs for older adults, drawing on a recent position statement from the Geriatric Committee of the International Union of Basic and Clinical Pharmacologists that she chairs. Prof Heather Allore, Yale, USA, will share her expertise in biostatistics in aging research, to demonstrate design and analysis techniques that can untangle the relationship between age, sex and frailty, applicable to biological and health sciences research. Prof Paula Rochon, University of Toronto, Canada, will highlight the importance of disaggregated data on sex, age and frailty to inform research, health care and policy for older adults. Finally, Prof Susan Howlett, Dalhousie, Canada, who pioneered research in the intersection of age, sex and frailty, will lead the discussion. Women’s Issues Interest Group Sponsored Symposium
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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 | 0.072 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 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".