The utility of remote cognitive screening tools in identifying cognitive impairment in older surgical patients: An observational cohort study
Bibliographic record
Abstract
STUDY OBJECTIVES: To determine the prevalence of suspected cognitive impairment using the Centers for Disease Control and Prevention (CDC) cognitive question, Ascertain Dementia Eight-item Questionnaire (AD8), Modified Telephone Interview for Cognitive Status (TICS-M), and Telephone Montreal Cognitive Assessment (T-MoCA), the agreement between each tool beyond chance, and the risk factors associated with a positive screen. DESIGN: Multicenter prospective study. SETTING: Remote preoperative assessments. PATIENTS: 307 non-cardiac surgical patients aged ≥65 years. MEASUREMENTS: Prevalence, Cohen's kappa (κ). MAIN RESULTS: The T-MoCA detected the highest prevalence of suspected cognitive impairment (28%), followed by the AD8 (17%), CDC cognitive question (9%), and TICS-M (6%). The four screening tools showed poor agreement beyond chance with one another, with the CDC cognitive question and AD8 approaching the threshold for weak agreement (κ = 0.39). Depression was associated with screening positive on the CDC cognitive question (OR: 2.81; 95% CI: 1.04, 7.68). Obstructive sleep apnea (OSA) (OR: 3.10; 95% CI: 1.26, 7.71) and functional disability (OR: 3.74; 95% CI: 1.34, 11.11) were associated with a positive AD8 screen. Older age (OR: 1.56; 95% CI: 1.01, 2.41), male sex (OR: 3.08; 95% CI: 1.09, 9.40), and higher pain level (OR: 1.21; 95% CI: 1.01, 1.47) were associated with a positive TICS-M screen. Similarly, older age (OR: 1.33; 95% CI: 1.03, 1.73), male sex (OR: 2.02; 95% CI: 1.09, 3.83), and higher pain level (OR: 1.15; 95% CI: 1.02, 1.30) were associated with a positive T-MoCA screen. CONCLUSIONS: The CDC cognitive question, AD8, TICS-M, and T-MoCA were easily implemented during preoperative assessment among older surgical patients. OSA, functional disability, and depression were associated with complaints on the CDC cognitive question and AD8. Older age, male sex, and higher pain level were associated with screening positive on the TICS-M and T-MoCA. Early remote cognitive screening may enhance risk stratification of vulnerable patients.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".