Use of nurse-observed symptoms of delirium in long-term care: effects on prevalence and outcomes of delirium
Bibliographic record
Abstract
BACKGROUND: Previous studies have reported that nurse detection of delirium has low sensitivity compared to a research diagnosis. As yet, no study has examined the use of nurse-observed delirium symptoms combined with research-observed delirium symptoms to diagnose delirium. Our specific aims were: (1) to describe the effect of using nurse-observed symptoms on the prevalence of delirium symptoms and diagnoses in long-term care (LTC) facilities, and (2) to compare the predictive validity of delirium diagnoses based on the use of research-observed symptoms alone with those based on research-observed and nurse-observed symptoms. METHODS: Residents aged 65 years and over of seven LTC facilities were recruited into a prospective study. Using the Confusion Assessment Method (CAM), research assistants (RAs) interviewed residents and nurses to assess delirium symptoms. Delirium symptoms were also abstracted independently from nursing notes. Outcomes measured at five month follow-up were: death, the Hierarchic Dementia Scale (HDS), the Barthel ADL scale, and a composite outcome measure (death, or a 10-point decline in either the HDS or the ADL score). RESULTS: The prevalence of delirium among 235 LTC residents increased from 14.0% (using research-observed symptoms only) to 24.7% (using research- and nurse-observed symptoms). The relative risks (and 95% confidence intervals) for prediction of the composite outcome, after adjustment for covariates, were: 1.43 (0.88, 1.96) for delirium using research-observed symptoms only; 1.77 (1.13, 2.28) for delirium using research- and nurse-observed symptoms, in comparison with no delirium. CONCLUSIONS: The inclusion of delirium symptoms observed by nurses not only increases the detection of delirium in LTC facilities but improves the prediction of outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".