Cognitive Dysfunction, Medication Management, and the Risk of Readmission in Hospital Inpatients
Bibliographic record
Abstract
OBJECTIVES: To determine whether cognitive dysfunction, in particular impaired executive function, may be a risk factor for early readmission in older adults independently managing their medications. DESIGN: Prospective observational study. SETTING: Tertiary hospital. PARTICIPANTS: Individuals aged 65 years and older discharged to home from the medicine service of a tertiary hospital (N = 452). MEASUREMENTS: Participants underwent a cognitive assessment including the Short Blessed Test (SBT), the executive function component of the Montreal Cognitive Assessment, and the Trail-Making Test Part B (TMT-B). Hospital use and demographic data were obtained. A logistic regression model was used to fit the likelihood of readmission on the basis of participant characteristics, medication management, and cognitive performance. Likelihood of hospital readmission within 30 days was determined. RESULTS: For participants managing medications themselves, adjusted 30-day odds of readmission increased 13% on average with each point decrease in SBT score (P = .003) and 9% on average with each 0.01 decrease in TMT-B score (P = .02). For participants who were independent in medication management with more than seven medications, the odds of 30-day readmission increased 16% on average with each point decrease in SBT score (P = .01) and 15% on average with each 0.01 decrease in TMT-B score (P = .03). CONCLUSION: Cognitive dysfunction, particularly executive dysfunction, is a risk factor for readmission in individuals managing their own medications. This risk is greater in individuals taking more than seven medications. The interaction of cognitive function, medication management, and number of medications may enhance risk-stratification efforts to identify individuals at risk of early readmission.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".