Can you see frailty? An exploratory study of the use of a patient photograph in the transcatheter aortic valve implantation programme
Bibliographic record
Abstract
BACKGROUND: Frailty is an important consideration in the assessment of transcatheter aortic valve implantation patients. The documentation of a patient photograph to augment the objective measurement of frailty has been adopted by some transcatheter aortic valve implantation multidisciplinary (TAVI) programmes. METHODS: We used a prospective two-part multimethod study design. In part A, we examined the concordance between the Essential Frailty Toolset (EFT) and the score attributed by healthcare professionals based on visual rating of photographs using kappa estimates and linear regression. In part B, we conducted a content analysis qualitative study to elicit information about how the TAVI multidisciplinary team used photographs to form impressions about frailty. FINDINGS: Part A: 94 healthcare professionals (registered nurses/allied health 65%; physicians 35%) rated 40 representative photographs (women 42.5%; mean age 83.4±7.5; mobility aid 40%) between 0 (robust) and 5 (very frail). The estimate of weighted kappa was 0.2575 (95% confidence interval 0.082-0.433), indicating fair agreement between median healthcare professional visual and EFT score, especially when the EFT was 1 or 4. There was significant discordance among raters (kappa estimate 0.110, 95% confidence interval 0.079-0.141). Age, sex and mobility aid did not have a significant effect on score discordance. Part B: 12 members of the TAVI multidisciplinary team (registered nurses 27.5%; physicians 72.5%) were shown a series of six representative patient photographs. The following themes emerged from the data: (a) looking at the outside; (b) thinking about the inside; (c) use but with caution; and (d) a better approach. CONCLUSION: A patient photograph offers complementary information to the multimodality assessment of TAVI patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".