Rationale and Design of the TARGET-EFT Trial: Multicomponent Intervention for Frail and Pre-frail Patients Hospitalized with Acute Cardiac Conditions
Bibliographic record
Abstract
BACKGROUND: With the aging population and rising rates of cardiovascular disease (CVD), cardiologists and cardiac surgeons are encountering a growing number of frail older patients that have complex cardiac and non-cardiac issues. Measuring frailty provides valuable prognostic information to help personalize treatment decisions. However, there is minimal evidence on multicomponent frailty interventions in this setting. The TARGET-EFT (The MulTicomponent Acute Intervention in FRail GEriatric PaTients with cardiovascular disease using the Essential Frailty Toolset) trial aims to target physical and non-physical frailty deficits to improve health-related quality of life and hospital-acquired disability in frail patients hospitalized with CVD. METHODS: The TARGET-EFT trial is a single-center parallel-group randomized clinical trial in frail and pre-frail older adults ≥65 years admitted to the cardiovascular unit (CVU) at the Jewish General Hospital, Montreal, Quebec. The trial will compare usual inpatient care to a multicomponent intervention targeting physical weakness, cognitive impairment, malnutrition, and anemia. Outcomes of interest in both groups will be assessed at three time points: (1) study enrollment, (2) discharge from the CVU, and (3) 30 days after hospital discharge. CONCLUSIONS: The overarching goal is to treat patients' frailty in parallel with their CVD, and in doing so, optimize patient functional losses while in-hospital and shortly thereafter. The results of this trial will inform best practices for patient-centered care in this vulnerable patient 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 | 0.043 | 0.033 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".