Frailty in the pre-surgical heart failure population: A comparison of clinician scoring, to use of the Modified Fried Frailty Assessment tool, in predicting frailty
Bibliographic record
Abstract
Purpose of Project: The primary aim of this project was to evaluate the effectiveness of subjective frailty assessments in predicting frailty, as measured by postoperative intensive care unit length of stays, using the Modified Fried Frailty Assessment to use of an objective frailty assessment tool. Methodology: Patients enrolled in the project underwent evaluation with the Montreal Cognitive Assessment tool before a subjective assessment by the heart failure attending and finally testing with the Modified Fried Frailty Assessment tool. The attendings were provided with the MoCA score but blinded to the results of the formal frailty assessment. Data was collected on subjective and objective frailty scoring and Montreal Cognitive Assessment scoring. Descriptive statistics were used to analyze the data for comparison between the two assessment types. Results: This project's results demonstrated that using an objective tool, such as the Modified Fried Frailty Assessment tool, is superior to subjective assessments performed by physicians. Implications for Practice: This project confirms the results of previous studies and lends validity to the need for continued implementation of a formalized frailty assessment protocol in every institution that provides surgical therapies for those with advanced heart failure.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".