Delirium assessment in postoperative patients: Validation of the Portuguese version of the Nursing Delirium Screening Scale in critical care
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aim of this study was to validate the Portuguese version of the Nursing Delirium Screening Scale (Nu-DESC) for use in critical care settings. METHODS: We simultaneously and independently evaluated all postoperative patients admitted to a surgical Intensive Care Unit (SICU) over a 1-month period for delirium, using the Portuguese versions of both the Nu-DESC and the Intensive Care Delirium Screening Checklist (ICDSC) within 24 hours of admission by both the research staff physician and one bedside nurse. We determined the diagnostic accuracy of the Nu-DESC using sensitivity, specificity and ROC curve analyses. We assessed reliability between nurses and the research staff physician for Nu-DESC by intraclass correlation coefficient (ICC). We assessed agreement and reliability between Nu-DESC and ICDSC by overall and specific proportions of agreement and by kappa statistics. RESULTS: Based on the ICDSC, we diagnosed delirium in 12 of the 78 patients. Reliability between nurses and the staff physician for total Nu-DESC score was high. Agreement between nurses and staff physician in the delirium diagnosis was perfect. The proportion of overall agreement between Nu-DESC and ICDSC in the delirium diagnosis was 0.88 and the kappa ranged from 0.79 to 0.93. Nu-DESC Sensitivity was 100 and specificity was 86%. CONCLUSIONS: The Portuguese version of the Nu-DESC appears to be an accurate and reliable assessment and monitoring instrument for delirium in critical care settings.
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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 | 0.006 | 0.030 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".