The prevalence of cognitive impairment in emergency general surgery
Bibliographic record
Abstract
OBJECTIVES: 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 Montreal Cognitive Assessment tool (MoCA) in acute general surgery. 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 assessed them for baseline demographic data. They each underwent a MoCA assessment. RESULTS: We collected data on 245 people, mean age 76.9 years (8.1, standard deviation), 136 (55.5%) were women. Of these 201 completed the MoCA test, mean score of 18.9 and median score 20 (range 0-30). There were 37 (15.1%) MoCA scores in the normal range (≥26) and 44 (18%) people were unable to attempt (or complete) the MoCA. Increasing age (p < 0.01) but not sex (p = 0.14) predicted an abnormal MoCA. Considering only the 44 people who were unable to attempt the MoCA assessment, 11 (25%) were known to have a diagnosis of dementia, 9 (20.5%) were too unwell and the remainder unable to complete the assessment to due pre-existing disability. CONCLUSIONS: In a representative UK wide population, a high proportion of older people admitted with an acute general surgical problem had cognitive impairment when assessed using the MoCA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".