Web-Based Software Applications for Frailty Assessment in Older Adults: Current Status and Insights Into Future Development
Bibliographic record
Abstract
Abstract Background: A crucial aspect of continued senior care is the early detection and management of frailty. Developing reliable and secure electronic frailty assessment tools can benefit virtual appointments, a need especially apparent since the COVID-19 pandemic. An emerging effort has targeted web-based software applications to improve accessibility and usage. Methods: We conducted an environmental scan through MEDLINE and Google searches (last updated on June 1st, 2021) to identify currently available web applications, each of which was evaluated and assigned a rating score based on eight featured categories.Results: Twelve web-based frailty assessment applications were found, chiefly provided by the USA (50%) or European countries (42%) and focused on frailty grading and outcome prediction for specific patient groups (58%). The categories that scored well among the applications included the User Interface (2.67/3) and the Cost (2.75/3). Other categories had a mean score of 1.5 or lower. The least developed features in the existing web applications included Data Saving.Conclusions: This is the first study that has compiled a comprehensive list of frailty assessments available online, described their usage and evaluated their advantages and limitations. The study emphasized several essential features with future web application development to support early detection and management of frailty with virtual care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".