Incidence of postoperative cognitive dysfunction in older adults: a prospective cohort study using a web-based Montreal cognitive assessment application
Bibliographic record
Abstract
Thailand is transitioning into a super-aged society, and its growing elderly population faces an increased risk of postoperative cognitive dysfunction (POCD). Despite its association with reduced quality of life and heightened mortality, POCD remains underdiagnosed. This study aimed to evaluate the incidence of POCD using a web-based Montreal Cognitive Assessment application (Auto-MoCA) at 1 week postoperatively. We also compared characteristics between patients with and without POCD and examined recovery patterns at 3 months. We conducted a prospective, descriptive cohort study at a university hospital in Bangkok, enrolling Thai-speaking patients aged ≥ 60 years who underwent major elective surgery. Cognitive function was repeatedly assessed via the Auto-MoCA application at baseline, 1 week, and 3 months. POCD was defined as a decrease of ≥ 2 points from baseline. Among 169 enrolled patients, 111 completed the 1-week assessment. POCD occurred in 40.5%, with significant declines in visuospatial, attention, language, abstraction, and orientation domains. Cardiovascular disease was a significant risk factor. By 3 months, 85 patients repeated the Auto-MoCA test. Of these, 34 (40.0%) had developed POCD compared to their baseline results, with 42.9% of affected patients recovered, primarily demonstrating improvements in visuospatial, attention, and delayed recall domains. Postoperative cognitive dysfunction (POCD) was common in older adults, affecting over 40% of patients at both 1 week and 3 months after surgery. With Cardiovascular disease emerged as a significant risk factor. However, nearly half of those affected showed cognitive recovery by 3 months. Underscoring the utility of digital cognitive assessment tools, such as the Auto-MoCA application, and highlight the need for future interventional research aiming to facilitate cognitive recovery in older adults following surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".