52DESIGNING A FRAILTY SCREENING TOOLKIT TO TRIAGE PATIENTS SEEN IN TRANSCATHERTER AORTIC VALVE IMPLANTATION (TAVI) CLINIC
Bibliographic record
Abstract
Introduction: Frailty is associated with increased mortality and morbidity in patients undergoing a TAVI (Martin, Sperrin, Ludman, BMJ Open, 2018, 1–9). NICE guidance promotes a shared decision-making model when making treatment choices in the frail elderly with symptomatic aortic stenosis, which should involve a Multi-Disciplinary Team including a specialist in elderly medicine (IPG586, 2017). With growing demands on frailty specialists, we need to ensure that we review the appropriate cases so that our time is used effectively. Methods: We created a pro-forma to identify patients who could benefit from geriatrician input. The TAVI ANP’s recorded data during 5 months of clinics. Acceptance criteria for geriatric referral included patients with either a Katz ADL score <4, age over 90, Mini Cog score of 2 or below and moderately or severely frail on Edmonton Frailty Scale (EFS). A Katz ADL score of 4 or less was used to highlight dependency as it is a validated marker of mortality in TAVI patients (Rogers, Alraises, Moussa, Am J Cardiol, 2018, 121, 850–855). The EFS was chosen as a frailty screen as it is validated in acute coronary syndrome and for use by non-geriatricians (Judith, Partridge, Harari, Age and Ageing, 2012, 41, 142–147). Results: 60 patients, 57% male with a median age of 82 (range 65–93). All patients lived in their own home with four requiring carers. 16 patients were highlighted as requiring a referral (7 > 90, 2 EFS moderately frail, 9 Mini Cog <2, 6 Katz ADL <4). The EFS flagged 8 as mildly frail and 14 as vulnerable, with only 7 of these 22 patients captured by the other referral criteria. Conclusion: Using the EFS alone to triage patients is not adequate. We need to continue to screen across the variety of domains including frailty, dependence and cognition, as some patients only triggered referral in one area. We have adapted the pro-forma to include a Clinical Frailty Score alongside the EFS to compare scores and analyse which score should be used in future practice. This project has allowed us to establish the potential demand on a geriatrician involved in a TAVI service. It has allowed us to share our knowledge of frailty with the TAVI team and create a screening tool to help to triage those who would benefit from a Comprehensive Geriatric Assessment.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".