EFRAILTY.ORG: ANALYSIS OF INITIAL USERS
Bibliographic record
Abstract
Abstract In March 2024, eFrailty.org was launched to the public with the goal of making frailty assessment tools accessible to clinicians and researchers. We report the uptake and initial user experiences of eFrailty between January 1, 2024 and July 31, 2024, as measured by Google Analytics. eFrailty was viewed over 23,000 times by more than 5,400 users. More than 1000 users (18%) returned after their initial visit. There were 4 distinct spikes in usage coinciding with the airing of a GeriPal Podcast episode, two international geriatrics conferences, and publication of a NEJM review article—all of which mentioned eFrailty. Analytics data indicated the site was accessed from over 60 different countries. The highest number of users were from the United States (2,400 users), followed by China (802), and Canada (522). The most frequently visited site feature was the ‘Help Me Choose a Frailty Tool’ page, which guides the user through a series of questions to identify an assessment tool that is best aligned to their purpose and available information. Of the 15 validated frailty assessment tools featured, the three most frequently viewed were the CGA-FI (2,105 views), Clinical Frailty Scale (709), and FRAIL Scale (680). User feedback was gathered informally from local and international colleagues using the site, and via e-mails sent through the site’s ‘Contact Us’ link. In response to feedback about the CGA-FI, the authors created a dedicated instruction manual. These initial data suggest the utility of eFrailty website for clinical care, research, and education.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".