192Can we Assess Visuospatial Function Verbally in Older Medical Inpatients?
Bibliographic record
Abstract
Commonly used tests of visuospatial function generally require a patient to use a pen and paper, e.g. the Clock-Drawing Test (CDT) or the Intersecting Pentagons Test (IPT). These tests can be challenging for older patients, particularly those with visual impairment, upper limb impairment, or fatigue associated with acute illness. We aimed to assess if a novel verbal test would correlate with IPT and CDT and hence be a potential alternative method of measuring visuospatial function in older medical inpatients. We developed a verbal test called the Environmental Visuospatial Questions Test (EVSQ) which included questions pertaining to a patient’s environment (e.g. which is closer to you, the window or the door?). As part of a study of delirium in older medical inpatients, participants were assessed within 36 hours of admission using EVSQ, CDT and IPT. Patients also underwent brief cognitive testing using the Six-item Cognitive Impairment Test (6-CIT). Spearman’s Rho was used to calculate the correlation between EVSQ and the visuospatial tests (CDT and IPT). We also examined correlation between each of the three tests and the 6-CIT respectively. Testing was conducted in 470 participants (median age 81 years, 50.4% female). Correlation between EVSQ and each of the visuospatial tests was weak (CDT 0.298, p < 0.001; IPT 0.136, p < 0.01). IPT and CDT had higher correlation (0.414, p < 0.001), yet it was still low. Correlation for each of the three tests was higher with 6-CIT, though remained low to moderate (IPT/6-CIT −0.419, p < 0.001; EVSQ/6-CIT −0.446, p < 0.001; CDT/6-CIT −0.576, p < 0.001). Correlation was weak between EVSQ and the commonly used visuospatial tests, but also between the two visuospatial tests (IPT and CDT). Correlation was higher with the 6-CIT for all the tests, especially CDT. This highlights that other factors, including cognitive impairment in other domains, are likely to affect visuospatial test performance.
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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 | 0.002 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".