The Utility of Psoas Muscle Assessment in Predicting Frailty in Patients Undergoing Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Background. The rise in an ageing population has resulted in an increase in the prevalence of aortic stenosis. With the advent and rapid expansion in the use of transcatheter aortic valve replacements (TAVRs), patients with severe aortic stenosis, traditionally thought too high risk for surgical intervention, are now being treated with generally favourable results. Frailty is an important factor in determining outcome after a TAVR, and an assessment of frailty is fundamental in the identification of appropriate patients to treat. Objective. The objective of the study was to identify if the psoas muscle area is associated with frailty in TAVR patients and outcome after intervention. Method. In this prospective study, we measured outcomes of 62 patients who underwent TAVR procedures against the psoas muscle area and the Reported Edmonton Frail Scale (REFS). Our aim was to assess if psoas muscle assessment can be used as a simple method to predict frailty in our population group. Results. A total of 60 patients met the study criteria. Mean psoas-lumbar vertebral index was 0.61, with a lower value in the frail group. There was not a statistically significant correlation between the psoas measures, REFS score (indicative of frailty), and mortality. However, there was a statistically significant relationship between the psoas size and REFS score ( p=0.043 ). Conclusion. Psoas assessment can be useful in providing additional information when planning for patients to undergo a TAVR and can be used as a screening tool to help identify frail patients within this high-risk group.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".