FRAILTY ASSESSMENT FROM PAPER TO ONLINE: THE E-FI-CGA WEB APP
Bibliographic record
Abstract
Abstract Background Early frailty assessment is crucial for improving care for older adults through enhancing frailty-informed care planning and execution. To increase the access and use of the established electronic Frailty Index based on the Comprehensive Geriatric Assessment tool, we developed an online software application: the web-based eFI-CGA. Methods End-user requirements primarily adapted the standalone version of the eFI-CGA and covered assessment competency, interface familiarity, accessibility and convenient use by front-line health professionals. Web app-bounded features included user-account authentication and authorization, user-space management, record-search and retrieval, and functions for data management, analysis and display. The web app development used the Microsoft Azure web server with the ASP.NET framework, combined Webforms and Model-View-Controller architecture, and C# programming language with standard client-side libraries incorporating scripting and mark-up languages. Results The web-based eFI-CGA was technically released and accessible online (efi-cga). Tests on the web pages for all the web pages (e.g., Home, Signup Login, Assessment, Search, and Analysis) using large sizes of systematically designed test data and mocked patient cohorts showed that the web software functions meet the requirements with 100% accuracy. Conclusion The web-based eFI-CGA provides a valuable remote frailty assessment and information retrieval method for healthcare professionals, promoting effective multidisciplinary integrated care of older adults. Further work is needed to allow the safe use of the web app. We are also planning a patient-oriented frailty assessment tool for community-dwelling older adults.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".