Validation of the virtual measurement of the Essential Frailty Toolset
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background The Essential Frailty Toolset (EFT) is a valid measurement of frailty in people with heart valve disease. COVID-19 has prompted the transition to virtual health consultations and necessitates the validation of the virtual assessment of frailty. Methods We conducted a prospective observational cohort study to compare the measurement of EFT in person and virtual format within a maximum 2-week window of repeated measurement. The weighted Kappa tests was used to measure the agreement of EFT scores between assessments; we explored the effect of the sequence of measurement using the Cochran-Mantel-Haenszel statistic to test the general association between the timing of measurement and differences of EFT score. Results We recruited a sample of 49 patients, with a mean age of 81 ± 7 years, including 29 men (59.2%); the primary valvular heart diseases were aortic stenosis (n=40, 81.6%), mitral regurgitation (n=2, 4.1%) and tricuspid regurgitation (n=7, 14.3%). The virtual measurement of frailty was conducted using a standardised protocol. The platform for virtual connection selected by patients was FaceTime (n=20, 40.8%) and Zoom (n=29, 59.2%); the median (IQR) number of days between the in-person and the virtual assessment was 5 (3,10). The weighted Kappa estimate was 0.69 (95% CI 0.55, 0.82), illustrating a strong agreement between the separate scores obtained. The test for the general association was non-significant (p=0.82), indicating a lack of evidence for detecting an association between EFT scores and chronological order of assessment. Conclusion The EFT can be reliably measured virtual in older patients with valvular heart disease to inform clinical 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 | 0.049 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".