Nu-DESC DK: the Danish version of the nursing delirium screening scale (nu-DESC)
Bibliographic record
Abstract
BACKGROUND: Delirium is one of the most common complications among elderly hospitalized patients, postoperative patients and patients on intensive care units with a prevalence between 11 and 80%. Delirium is associated with higher morbidity and mortality. Reliable instruments are required to detect delirium at an early time point. The Nursing-Delirium Screening Scale (Nu-DESC) is a screening tool with high sensitivity and good specificity. However, there is currently no official translation after ISPOR guidelines of any Danish delirium assessment tools available. Thereby hampering the implementation of 2017 ESA-Guidelines on postoperative Delirium in the clinical routine. The aim of this study is to provide an official translation and evaluation of the Nu-DESC into Danish following the ISPOR process. METHODS: The Nu-DESC was translated after International Society for Pharmacoecomonics and Outcome Research (ISPOR) guidelines to Danish after permission of the original author, and is evaluated by medical staff and finally approved by the original author. RESULTS: All steps of the ISPOR guideline were consecutively followed, without any major problems. The evaluation of the Nu-DESC DK regarding its intelligibility and feasibility showed no statistically significant differences between nurses and medical doctors ratings. The translation was authorized and approved by the original author. CONCLUSION: This study provides the Nu-DESC DK, an official Danish delirium screening instrument, which can detect all psychomotor types of delirium.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".