Recognizing acute delirium as part of your routine [RADAR]: a validation study
Bibliographic record
Abstract
BACKGROUND: Although detection of delirium using the current tools is excellent in research settings, in routine clinical practice, this is not the case. Together with nursing staff, we developed a screening tool (RADAR) to address certain limitations of existing tools, notably administration time, ease-of-use and generalizability. The purpose of this study was not only to evaluate the validity and reliability of RADAR but also to gauge its acceptability among the nursing staff in two different clinical settings. METHODS: This was a validation study conducted on three units of an acute care hospital (medical, cardiology and coronary care) and five units of a long-term care facility. A total of 142 patients and 51 residents aged 65 and over, with or without dementia, participated in the study and 139 nurses were recruited and trained to use the RADAR tool. Data on each patient/resident was collected over a 12-hour period. The nursing staff and researchers administered RADAR during the scheduled distribution of medication. Researchers used the Confusion Assessment Method to determine the presence of delirium symptoms. Delirium itself was defined as meeting the criteria for DMS-IV-TR delirium. Inter-rater reliability, convergent, and concurrent validity of RADAR were assessed. At study end, 103 (74%) members of the nursing staff completed the RADAR feasibility and acceptability questionnaire. RESULTS: Percentages of agreement between RADAR items that bedside nurses administered and those research assistants administered varied from 82% to 98%. When compared with DSM-IV-TR criterion-defined delirium, RADAR had a sensitivity of 73% and a specificity of 67%. Participating nursing staff took about seven seconds on average, to complete the tool and it was very well received (≥98%) overall. CONCLUSIONS: The RADAR tool proved to be efficient, reliable, sensitive and very well accepted by nursing staff. Consequently, it becomes an appropriate new option for delirium screening among older adults, with or without cognitive impairment, in both hospitals and nursing homes. Further projects are currently underway to validate the RADAR among middle-aged adults, as well as in newer clinical settings; home care, emergency department, medical intensive care unit, and palliative care.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".