TOWARD DEVELOPING PATIENT-ORIENTED WEBSITES FOR EARLY DETECTION AND MANAGEMENT OF FRAILTY
Bibliographic record
Abstract
Abstract Understanding frailty status of older adults before a doctor’s visit can result in more effective care planning, where web technology can play role. We conducted a study engaging community-dwelling older adults and caregivers to better understand their perspectives about developing patient-oriented frailty websites. Our objectives are to describe the project activities and share the insights learned from potential end-users of the website. We first organized an online educational symposium consisting of six invited talks on patient-oriented frailty management given by experts of frailty and older adult care. Audience of the symposium was recruited through newsletters and academic, community, and patient association platforms. Clinicians, researchers, caregivers, and community-dwelling older adults across Canada participated (n=300). Members from the symposium (n=14) participated in two virtual discussion sessions with open-ended questions. Discussions highlighted aspects, including the function, usage, and feasibility of a frailty website; security and privacy of online health data; patient-physician interactions; and existing technologies and resources supporting patient-oriented frailty management for successful aging in place. Our findings revealed the needs, barriers, and possible solutions with developing patient-oriented frailty websites. The effort towards building frailty websites that patients can use will benefit future development and implementation to identify people at risk of frailty and facilitate real-time monitoring for personalized and preventative care.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".