Is hospitalization a risk factor for cognitive decline in older age adults?
Bibliographic record
Abstract
Objectives:Many studies document cognitive decline following specific types of acute illness hospitalizations (AIH) such as surgery, critical care, or those complicated by delirium. However, cognitive decline may be a complication following all types of AIH. This systematic review will summarize longitudinal observational studies documenting cognitive changes following AIH in the majority admitted population and conduct meta-analysis (MA) to assess the quantitative effect of AIH on post-hospitalization cognitive decline (PHCD).Methods:We followed Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Selection criteria were defined to identify studies of older age adults exposed to AIH with cognitive measures. 6566 titles were screened. 46 reports were reviewed qualitatively, of which seven contributed data to the MA. Risk of bias was assessed using the Newcastle–Ottawa Scale.Results:The qualitative review suggested increased cognitive decline following AIH, but several reports were particularly vulnerable to bias. Domain-specific outcomes following AIH included declines in memory and processing speed. Increasing age and the severity of illness were the most consistent risk factors for PHCD. PHCD was supported by MA of seven eligible studies with 41,453 participants (Cohen’s d = −0.25, 95% CI [−0.02, −0.49] I2 35%).Conclusions:There is preliminary evidence that AIH exposure accelerates or triggers cognitive decline in the elderly patient. PHCD reported in specific contexts could be subsets of a larger phenomenon and caused by overlapping mechanisms. Future research must clarify the trajectory, clinical significance, and etiology of PHCD: a priority in the face of an aging population with increasing rates of both cognitive impairment and hospitalization.
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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 | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".