<p>The value of screening for cognition, depression, and frailty in patients referred for TAVI</p>
Bibliographic record
Abstract
Background: Current surgical risk assessment tools fall short of appreciating geriatric risk factors including cognitive deficits, depressive, and frailty symptoms that may worsen outcomes post-transcatheter aortic valve implantation (TAVI). This study hypothesized that a screening tool, SMARTIE, would improve detection of these risks pre-TAVI, and thus be predictive of postoperative delirium (POD) and 30-day mortality post-TAVI. Design: Prospective observational cohort study, using a historical cohort for comparison. Participants: A total of 234 patients (age: 82.2±6.7 years, 59.4% male) were included. Half were screened using SMARTIE. Methods: The SMARTIE cohort was assessed for cognitive deficits and depressive symptoms using the Mini-Cog test and PHQ-2, respectively. Measures of frailty included activities of daily living inventory, the Timed Up and Go test and grip strength. For the pre-SMARTIE cohort, we extracted cognitive deficits, depression and frailty symptoms from clinic charts. The incidence of POD and 30-day mortality were recorded. Bivariate chi-square analysis or t -tests were used to report associations between SMARTIE and pre-SMARTIE groups. Multivariable logistic regression models were employed to identify independent predictors of POD and 30-day mortality. Results: More patients were identified with cognitive deficits (χ 2 =11.73, p =0.001), depressive symptoms (χ 2 =8.15, p =0.004), and physical frailty (χ 2 =5.73, p =0.017) using SMARTIE. Cognitive deficits were an independent predictor of POD (OR: 8.4, p <0.01) and 30-day mortality (OR: 4.04, p =0.03). Conclusion: This study emphasized the value of screening for geriatric risk factors prior to TAVI by demonstrating that screening increased identification of at-risk patients. It also confirmed findings that cognitive deficits are predictive of POD and mortality following TAVI. Keywords: TAVI, cognition, depression, frailty
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".