Depression, Obstructive Sleep Apnea and Cognitive Impairment (DOC) Screen Completion Time Reflects Executive Function, Speed of Processing and Fluency
Bibliographic record
Abstract
ABSTRACT Background: The depression, obstructive sleep apnea and cognitive impairment (DOC) screen assesses three post-stroke comorbidities, but additional information may be gained from the time to complete the screen. Cognitive screening completion time is rarely used as an outcome measure. Objective: To assess DOC screen completion time as a predictor of cognitive impairment in stroke/transient ischemic attack clinics. Methods: Consecutive English-speaking stroke prevention clinic patients consented to undergo screening and neuropsychological testing (n = 437). DOC screen scores and times were compared to scores on the NINDS-CSC battery using multiple linear regression (controlling for age, sex, education and stroke severity) and receiver operating characteristic (ROC) curve analysis. Results: Completion time for the DOC screen was 3.8 ± 1.3 minutes. After accounting for covariates, the completion time was a significant predictor of the speed of processing (p = 0.002, 95% CI: −0.016 to −0.004), verbal fluency (p < 0.001, CI: −0.012 to −0.006) and executive function (p = 0.004, CI: −0.006 to −0.001), but not memory. Completion time above 5.5 minutes was associated with a high likelihood of impairment on executive and speed of processing tasks (likelihood ratios 3.9–5.2). Conclusions: DOC screen completion time is easy to collect in routine care. People needing over 5.5 minutes to be screened likely have deficits in executive functioning and speed of processing – areas commonly impaired, but challenging to screen for, after stroke. DOC screen time provides a simple, feasible approach to assess these under-identified cognitive impairments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".