PRESIDENTIAL SYMPOSIUM: DEVELOPING ACUTE CARE SERVICES FOR OLDER PEOPLE: GLOBAL PERSPECTIVES FOR THE NEXT DECADE
Bibliographic record
Abstract
The development of responsive and efficient acute care services for older people remains a high-priority focus around the world and into the next decade. This symposium showcases expertise from North America, Europe, Southeast Asia and Asia to review the latest evidence and experiences that improve the care for older people in hospitals globally, and provide an interactive opportunity for participants to share strategies from their local jurisdictions. After attending this session, participants will be able to: (a) list the steps of developing a geriatric program that is aligned with innovations (such as personalized medicine and big data analytics) in the future hospital; (b) describe how quality improvement can drive better frailty care; (c) give examples of how to improve delirium care; and (d) identify the characteristics of effective post-acute care services. The symposium speakers are recognized leaders in Geriatrics globally and locally, hence providing their glocal perspectives. All have solid track records of implementing system-based improvements on acute care services for older people. At the symposium, we will present cutting-edge, evidence-informed findings and experiences that will influence acute care services in the next decade. We plan to tailor to participants’ needs in identifying local improvement opportunities and sharing with them lessons leanred during knowledge-to-practice translation.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".