The utility of the Montreal cognitive assessment (MoCA) in detecting cognitive impairment in surgical populations – A systematic review and meta-analysis
Bibliographic record
Abstract
STUDY OBJECTIVE: To determine the diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) in detecting cognitive impairment (CI) and assess the association of MoCA scores with adverse postoperative outcomes in surgical populations. DESIGN: Systematic review and meta-analysis. SETTING: Perioperative setting. PATIENTS: Adults undergoing elective or emergent surgery screened for CI preoperatively using the MoCA. MEASUREMENTS: The outcomes included the diagnostic accuracy of the MoCA in screening for CI and the pooled prevalence of CI in various surgical populations. CI and its association with adverse events including delirium, hospital length-of-stay (LOS), postoperative complications, discharge destination, and mortality was determined. MAIN RESULTS: Twenty-six studies (5059 patients, 18 non-cardiac studies, 8 cardiac studies) were included. With a MoCA cut-off score of <26, the prevalence of preoperative CI was 48% (95% CI: 41%-54%). The MoCA had 0.87 (95% CI: 0.79-0.93) sensitivity, 0.72 (95% CI: 0.62-0.80) specificity, PPV of 0.74 (95% CI: 0.65-0.81), and NPV of 0.86 (95% CI: 0.77-0.92) when validated against Petersen criteria, the Diagnostic and Statistical Manual of Mental Disorders, or the National Institute on Aging and the Alzheimer's Association criteria to identify CI. Using the MoCA as a screening tool, the LOS was 3.75 (95% CI: -0.03-7.53, P = 0.05, not significant) days longer in the CI group after non-cardiac surgeries and 3.33 (95% CI: 1.24-5.41, P < 0.002) days longer after cardiac surgeries than the non-cognitively impaired group. CONCLUSIONS: MoCA had been validated in the surgical population. MoCA with a cut-off score of <26 was shown to have 87% sensitivity and 72% specificity in identifying CI. A positive screen in MoCA was associated with a 3-day longer hospital LOS in cardiac surgery in the CI group than in the non-CI group.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".