51 * THE PREVALENCE OF COGNITIVE IMPAIRMENT MEASURED USING THE MONTREAL COGNITIVE ASSESSMENT METHOD (MOCA) IN AN OLDER ACUTE GENERAL SURGICAL POPULATION
Bibliographic record
Abstract
Introduction: Rates of all surgical procedures are increasing at a faster rate than the population is ageing. However, this encouraging statistic, necessitates a robust evidence base. The epidemiological evidence base in acute general surgery in the older person is sparse. This is the first assessment of the prevalence of cognitive impairment measured using the MoCA in this setting. Methods: In three sites in Wales, England and Scotland comprising rural and urban populations, we studied consecutive patients aged over 65 years. We considered any older person admitted to the acute general surgical unit. We did not include patients with orthopaedic, urological, neurosurgical or vascular conditions. We assessed them for baseline demographic data. They each underwent a MoCA assessment. Permission was granted for the use of the MoCA in the research setting. We did not assess delirium. Results: We collected data on 220 people, mean age 77 years (range 65–99), 156 (56.1%) were women. Of these 189 completed the MoCA test, Median score 21 (range 0–30). There were 33 (17.1%) MoCA scores in the normal range (> = 26). Increasing age (p < 0.001) but not sex (p = 0.34) predicted an abnormal MoCA. Of the 41 (18.6%) people who were unable to complete the MoCA assessment, 22 were known to have a diagnosis of dementia, 15 were too unwell and the remainder unable to complete the assessment to due pre-existing disability, most commonly poor vision. Conclusions: In a representative UK wide population, over 80% of people aged over 65 years admitted with an acute general surgical problem had cognitive impairment when assessed using the MoCA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".