Digital clock drawing test metrics in older patients before and after endoscopy with sedation: An exploratory analysis
Bibliographic record
Abstract
BACKGROUND: In the postoperative period, clinically feasible instruments to monitor elderly patients' neurocognitive recovery and discharge-readiness, especially after short-stay procedures, are limited. Cognitive monitoring may be improved by a novel digital clock drawing test (dCDT). We screened for cognitive impairment with the 4 A Test (4AT) and then administered the dCDT pre and post short-stay procedure (endoscopy). The primary aim was to investigate whether the dCDT was sensitive to a change in cognitive status postendoscopy. We also investigated if preoperative cognitive status impacted postendoscopy dCDT variables. METHODS: We recruited 100 patients ≥65 years presenting for endoscopy day procedures at a single metropolitan hospital. Participants were assessed after admission and immediately before discharge from the hospital. We administered the 4AT, followed by both command and copy clock conditions of the dCDT. We analysed the total drawing time (dCDT time), as well as scored the drawn clock against the established Montreal Cognitive Assessment (MoCA) criteria both before and after endoscopy. RESULTS: Linear regression showed higher 4AT test scores (poorer performance) were associated with longer postoperative dCDT time (β = 5.6, p = 0.012) for the command condition after adjusting for preoperative baseline dCDT metrics, sex, age, and years of education. CONCLUSION: Postoperative dCDT time-based variables slowed in those with baseline cognitive impairment detected by the 4AT, but not for those without cognitive impairment. Our results suggest the dCDT, using the command mode, may help detect cognitive impairment in patients aged >65 years after elective endoscopy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".