The relationship between the Montreal Cognitive Assessment score and readmission for left ventricular assist device implant patients at Wellstar Health System
Bibliographic record
Abstract
There is a confirmed relationship between cognitive impairment and heart failure, and the Montreal Cognitive Assessment (MoCA) is a reliable measure of cognitive function in heart failure patients. An important measure of clinical quality for heart failure patients is unplanned hospital readmissions, and cognitive impairment can be a predictor for short-term outcomes in heart failure patients, including 30-day readmissions. This study examined the association between pre-LVAD (left ventricular assist device) implant MoCA score and 30-day post-implant readmission status in advanced heart failure patients. Available research indicates that lower MoCA scores for congestive heart failure patients, including LVAD patients, would indicate a higher risk of 30-day readmission. Participants included all patients who received an LVAD implantation during the study period at Wellstar Health System. Patients were given the Montreal Cognitive Assessment screening among other testing to determine acceptable candidacy for LVAD prior to implantation. The MoCA score most immediately preceding the LVAD implantation was used, as well as the 30-day readmission data post-LVAD implantation. A dichotomous logistic regression was performed with the continuous MoCA score as the independent variable, and 30-day readmission designated as Yes or No as the dependent variable. The target for the analysis was readmission. When analyzing the LVAD patient population from Wellstar Health System, the MoCA score is not strongly associated with 30-day readmission on initial LVAD implantation. The statistical model predicted everyone to not be readmitted. This model left 96.2% to 97.7% of the variance unexplained. The model misidentified all patients from the group that were readmitted within 30 days, which accounted for 28.3% of the population. Additional research is required to determine why this patient population differs from population in existing research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".