The utility of the Ascertain Dementia Eight-item Questionnaire (AD8) and Mini-Cog in detecting cognitive impairment in older surgical patients – The Detect CI study
Bibliographic record
Abstract
BACKGROUND: Brain health is often overlooked before surgery, missing an opportunity to identify at-risk patients. This study aimed to (1) examine the diagnostic performance of two ultra-rapid cognitive screening tools, the Ascertain Dementia Eight-item Questionnaire (AD8) and Mini-Cog, against a tool validated in surgical populations, the Montreal Cognitive Assessment (MoCA); and (2) compare preoperative patient-centered assessments and postoperative outcomes between those with and without cognitive impairment (CI). METHODS: CI was classified by scoring ≥2 on the AD8, ≤2 on the Mini-Cog, and/or ≤25 on the MoCA in non-cardiac patients ≥65 years old. RESULTS: Of 394 participants, 35 % had preoperative CI on the MoCA, 15 % on the AD8, and 12 % on the Mini-Cog. Both the AD8 and Mini-Cog demonstrated moderate area under the curve, with superior specificity over sensitivity. In contrast to the Mini-Cog, participants with CI on the MoCA and AD8 reported poorer preoperative patient-centered assessments than those without. Specifically, the AD8 was associated with poorer functional disability, frailty, anxiety and/or depression, pain level, sleep quality, and quality of life. Contrary to the MoCA and Mini-Cog, CI on the AD8 was associated with higher all-cause complications (15.1 % vs. 3.7 %, P = 0.003), emergency department visits (11.3 % vs. 2.5 %, P = 0.007), and composite adverse outcomes (15.1 % vs. 4.6 %, P = 0.008) at 90 days. CONCLUSIONS: Although the AD8 and Mini-Cog demonstrated comparable diagnostic accuracy for CI, the AD8 provided additional insights into preoperative patient-centered assessments and 90-day adverse outcomes. Our study emphasizes the importance of preoperative screening for CI, highlighting the AD8 as a valuable tool.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".