The Montreal Cognitive Assessment as a Screening Tool for Preoperative Cognitive Impairment in Geriatric Patients.
Bibliographic record
Abstract
OBJECTIVE: Study the prevalence of cognitive impairment using the Montreal Cognitive Assessment (MoCA), its relationship to physiologic age-related change, and the preoperative drugs used in geriatric patients. MATERIAL AND METHOD: At the preoperative visit, the co-researchers invited 322 general/vascular patients (190 male, 132 female) and 260 urological patients (220 male, 40 female) who met the inclusion criteria and were scheduled for elective surgery to join the study. They went for the MoCA interview, and their preoperative drugs used were recorded in a medication reconciliation file. A cut-off score 24 or above was considered normal. RESULTS: Ninety-two general/vascular and 126 urological patients had taken drugs before admission, but those did not show any correlation with the MoCA score. There were 231 and 91 general/vascular patients and 175 and 85 urological patients with formal education of less than six years and equal/more than six years respectively. The 286 and 36 general/vascular patients and 212 and 48 urological patients posted scores of less than 24 and equal/more than 24 respectively. Gender and education correlated positively and significantly with the score; however age proved negatively significant. CONCLUSION: The prevalence of cognitive impairment featured highly in preoperative geriatric patients. The gender age, and education but not preoperative drugs used affected cognitive function.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".