Die Nursing Delirium Screening Scale (Nu-DESC) - Richtlinienkonforme Übersetzung für den deutschsprachigen Raum
Bibliographic record
Abstract
BACKGROUND: Both in the recovery room as well as in the intensive care unit post-operative delirium is the most common psychiatric disease. The post-operative delirium is stated in literature to occur in 15 % to 50 % of patients, whereby up to 80 % of patients requiring intensive care with artificial respiration develop a delirium. The delirium correlates with the length of hospital stay and leads to a tripple rate of the six-month-mortality. Nu-DESC, developed by Gaudreau et al. is a measuring instrument for the clinical diagnostics of deliriums which is quickly operable, care-based and which can thus be easily integrated in everyday routine. The aim of this study was the translation of Nu-DESC from English as basis for the use in clinical research and routine. MATERIALS AND METHODS: The translation process was conducted in accordance with the internationally acknowledged guidelines of Translation and Cultural Adaptation of Patient Reported Outcomes Measures - Principles of Good Practice (PGP). An interim German version was developed from 3 independently devised translations, a back-translation of which was then conducted by a registered state-approved translator. The back-translation was then presented to the author of the original for evaluation. RESULTS: The back-translation of the German translation was authorised by the author of the original. On the basis of the cognitive debriefing results which were consistently very good to good, the translation process could be finalised and the final German version of Nu-DESC could be passed by the expert team. An evaluation of the German Nu-DESC regarding its practicability showed significant differences between doctors and nursing staff. CONCLUSION: The German version of Nu-DESC provides an instrument for evaluating the delirium in the area of clinical routine and research.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".