Application of the Uniform Data Set version 3 tele-adapted test battery (T-cog) for remote cognitive assessment preoperatively in older adults
Bibliographic record
Abstract
Introduction: Older adults undergoing surgery are at risk of postoperative neurocognitive disorders, prompting the need for preoperative cognitive screening in this population. Traditionally, cognitive screening has been conducted in-person using brief assessment tools such as the Montreal Cognitive Assessment (MoCA) or the Mini-Mental State Examination (MMSE). More comprehensive test batteries, such as the Uniform Data Set (UDS) Neuropsychological Battery, and its remote testing version, the Uniform Data Set version 3 tele-adapted test battery (UDS v3.0 T-cog), have been developed to assess cognitive decline in normal aging and disease conditions, but have not been applied in the perioperative setting. Methods: We assessed the feasibility of using this remote UDS v3.0 T-cog battery for preoperative cognitive assessment in 81 older adults 65+ scheduled for lower extremity joint replacement surgery. Results: Our results indicate that the UDS v3.0 T-cog achieves 99% completion rates and demonstrates high patient satisfaction. Further, we found 28% of subjects were cognitively impaired in this patient cohort. Discussion: These findings suggest that the UDS v3.0 T-cog is a feasible tool for assessing cognitive function in the older adult perioperative population. To our knowledge, this is the first study to apply this comprehensive remote test battery in the preoperative setting.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".