Delirium assessment in neuro‐critically ill patients: A validation study
Bibliographic record
Abstract
BACKGROUND: Delirium is underinvestigated in the neuro-critically ill, although the harmful effect of delirium is well established in patients in medical and surgical intensive care units (ICU).To detect delirium, a valid tool is needed. We hypothesized that delirium screening would be feasible in patients with acute brain injury and we aimed to validate and compare the Confusion Assessment Method for the ICU and the Intensive Care Delirium Screening Checklist against clinical International Classification of Diseases-10 criteria as reference. METHODS: Nurses assessed delirium using the Confusion Assessment Method for the ICU and Intensive Care Delirium Screening Checklist in adult patients with acute brain injury admitted to the Neurointensive care unit (Neuro-ICU), Copenhagen University Hospital, if their Richmond agitation-sedation scale score was -2 or above. As the reference, a team of psychiatrist assessed patients using the International Classification of Diseases-10 criteria. RESULTS: We enrolled 74 patients, of whom 25 (34%) were deemed unable to assess by the psychiatrists, leaving 49 (66%) for final analysis. Sensitivity and specificity for the Confusion Assessment Method for the ICU was 59% (95% CI: 41-75) and 56% (95% CI: 32-78), respectively, and 85% (95% CI: 70-94) and 75% (95% CI: 51-92), respectively, for the Intensive Care Delirium Screening Checklist. CONCLUSIONS: Our findings suggest that the Intensive Care Delirium Screening Checklist may be a valid tool and the Confusion Assessment Method for the ICU is less suitable for delirium detection for patients in the Neuro-ICU. In the neuro-critically ill, delirium screening is challenged by limited feasibility.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".