INTRODUCING EFRAILTY: SIMPLIFYING SELECTION OF FRAILTY ASSESSMENT TOOLS
Bibliographic record
Abstract
Abstract Health care providers recognize the importance of frailty assessment for older adults, but they may be unfamiliar with which frailty assessment tool to use. We sought to create an accessible website to assist clinicians in choosing an effective, evidence-based frailty screening tool. We selected commonly used frailty tools based on the literature and worked with a web designer to develop the eFrailty website prototype. A short description of each tool’s key features and estimated time for assessment is included for each frailty tool. An algorithm based on differences in patient characteristics, clinical scenarios, available information, and time for assessment was created to guide users. Modeled after the highly popular ePrognosis website, eFrailty is designed to guide clinicians to select the ideal frailty tool for their clinical context. The site prompts clinicians to choose between patients considering stressful treatment (e.g., major surgery), or patients with or without serious illness. Depending on available information, clinicians choose between ‘Self reports/records only,’ or ‘Performance tests available’ including cognitive screens or physical performance testing. Alternatively, Clinicians may use the eFrailty comparison table which builds on the work of several systematic reviews of frailty identification tools to easily select the best instrument for their patient. A recent addition to the site is a crosswalk to compare scores between different frailty assessment tools. Future directions for eFrailty include beta testing to gather clinician input from point of care use.
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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 | 0.043 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".