Five short screening tests in the detection of prevalent delirium: diagnostic accuracy and performance in different neurocognitive subgroups
Bibliographic record
Abstract
BACKGROUND: Delirium is prevalent and serious, yet remains under-recognised. Systematic screening could improve detection; however, consensus is lacking as to the best approach. Our aim was to assess the diagnostic accuracy of five simple cognitive tests in delirium screening: six-item cognitive impairment test (6-CIT), clock-drawing test, spatial span forwards, months of the year backwards (MOTYB) and intersecting pentagons (IPT). METHODS: A cross-sectional study was conducted. Within 36 h of admission, older medical patients were assessed for delirium using the Revised Delirium Rating Scale. They also underwent testing using the five cognitive tests outlined above. Sensitivity, specificity, positive and negative predictive values (PPV; NPV) were calculated for each method. Where appropriate, area under the receiver operating characteristic curve (AUC) was also calculated. RESULTS: Four hundred seventy patients were included, and 184 had delirium. Of the tests scored on a scale, the 6-CIT had the highest AUC (0.876), the optimum cut-off for delirium screening being 8/9 (sensitivity 89.9%, specificity 62.7%, NPV 91.2%, PPV 59.2%). The MOTYB, scored in a binary fashion, also performed well (sensitivity 84.6%, specificity 58.4%, NPV 87.4%, PPV 52.8). On discriminant analysis, 6-CIT was the only test to discriminate between patients with delirium and those with dementia (without delirium), Wilks' Lambda = 0.748, p < 0.001. CONCLUSION: The 6-CIT measures attention, temporal orientation and short-term memory and shows promise as a delirium screening test. This study suggests that it may also have potential in distinguishing the cognitive impairment of delirium from that of dementia in older patients. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".