Use of the Montreal Cognitive Assessment Test to Investigate the Prevalence of Mild Cognitive Impairment in the Elderly Elective Surgical Population
Bibliographic record
Abstract
Postoperative cognitive disorders are common in elderly patients. Pre-existing cognitive impairment including mild cognitive impairment may be an important risk factor for developing postoperative cognitive dysfunction and may not be detected in a standard preoperative interview, yet is not routinely sought. Our primary aim was to estimate the prevalence of mild cognitive impairment among elderly patients presenting to our hospital for elective surgery using a simple established screening tool: the Montreal Cognitive Assessment test. Secondarily, we wished to determine the proportion of patients with mild cognitive impairment who presented with this information available, the effect of increasing age on the prevalence of mild cognitive impairment and whether the timing and location of testing influenced results. We used the Montreal Cognitive Assessment test to screen preoperative patients aged 65 years and over. Our results suggested a potential prevalence of mild cognitive impairment of 56%, with prevalence increasing with age. No patients in the sample had a recorded diagnosis of mild cognitive impairment. Testing in either the preadmission clinic or on admission on the day of surgery yielded similar results. We found the Montreal Cognitive Assessment test to be a simple screening tool that was easily administered during the pre-admission visit.
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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 | 0.002 | 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".