Assessment of inattention in the context of delirium screening: one size does not fit all!
Bibliographic record
Abstract
BACKGROUND: Despite its high prevalence and deleterious consequences, delirium often goes undetected in older hospitalized patients and long-term care (LTC) residents. Inattention is a core symptom of this syndrome. The aim of this study was to explore the usefulness of ten simple and objective attention tests that would enable efficient delirium screening among this population. METHODS: This was a secondary analysis (n = 191) of a validation study conducted in one acute care hospital (ACH) and one LTC facility among older adults with, or without, cognitive impairment. The attention test tasks (n = 10) were drawn from the Concentration subscale the Hierarchic Dementia Scale (HDS). Delirium was defined as meeting the criteria for DSM-5 delirium. The Confusion Assessment Method (CAM) was used to determine the presence of delirium symptoms. RESULTS: The Months of the Year Backward (MOTYB) test, which 57% of participants completed successfully, showed the best balance between sensitivity and specificity (82.6%; 95% CI [61.2-95.0], and 62.5%; 95% CI [54.7-69.8] respectively) for the entire group. Subgroup analyses revealed that no test had both sensitivity and specificity over 50% in participants with cognitive impairment indicated in their medical chart. CONCLUSIONS: Our results revealed that these tests varied greatly in performance and none can be earmarked to become a single-item screening tool for delirium among older patients and residents with, or without, cognitive impairment. The presence of premorbid cognitive impairment may necessitate more extensive assessments of delirium, especially when a change in general status or mental state is observed.
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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 | 0.046 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".