Evaluating attention in delirium: A comparison of bedside tests of attention
Bibliographic record
Abstract
AIM: Impaired attention is a core diagnostic feature for delirium. The present study examined the discriminating properties for patients with delirium versus those with dementia and/or no neurocognitive disorder of four objective tests of attention: digit span, vigilance "A" test, serial 7s subtraction and months of the year backwards together with global clinical subjective rating of attention. METHODS: This as a prospective study of older patients admitted consecutively in a general hospital. Participants were assessed using the Confusion Assessment Method, Delirium Rating Scale-98 Revised and Montreal Cognitive Assessment scales, and months of the year backwards. Pre-existing dementia was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders fourth edition criteria. RESULTS: The sample consisted of 200 participants (mean age 81.1 ± 6.5 years; 50% women; pre-existing cognitive impairment in 126 [63%]). A total of 34 (17%) were identified with delirium (Confusion Assessment Method +). The five approaches to assessing attention had statistically significant correlations (P < 0.05). Discriminant analysis showed that clinical subjective rating of attention in conjunction with the months of the year backwards had the best discriminatory ability to identify Confusion Assessment Method-defined delirium, and to discriminate patients with delirium from those with dementia and/or normal cognition. Both of these approaches had high sensitivity, but modest specificity. CONCLUSION: Objective tests are useful for prediction of non-delirium, but lack specificity for a delirium diagnosis. Global attentional deficits were more indicative of delirium than deficits of specific domains of attention. Geriatr Gerontol Int 2016; 16: 1028-1035.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".