Software-guided (PREVEDEL) cognitive stimulation to prevent delirium in hospitalised older adults: study protocol
Bibliographic record
Abstract
BACKGROUND: Delirium is a clinical condition characterised by acute and fluctuating deterioration of the cognitive state, generally secondary to an acute pathology. Delirium is associated with negative outcomes in older adults, such as longer hospitalisations, higher mortality, and short and medium-term institutionalisation. Randomised clinical trials have shown that delirium is preventable through non-pharmacological prevention measures, decreasing its incidence by 30-50%. These interventions include promoting physical activity, facilitating the use of glasses and hearing aids, cognitive stimulation, and providing frequent reorientation of time and space, among others. These measures are currently seldom applied in hospitals in Chile and around the world for reasons including the heavy workload of clinical staff, the lack of trained personnel, and in general the absence of a systematic implementation processes. We developed a software called PREVEDEL, which includes non-pharmacological strategies such as cognitive stimulation, early mobilisation, orientation, and pain assessment. We propose a randomised clinical trial to evaluate whether cognitive stimulation guided by PREVEDEL software prevents delirium status (full/subsyndromal delirium) in hospitalised older adults. METHOD: A randomised controlled trial, with parallel, multicentre groups. We will recruite patients 65 years or older who have been hospitalised for less than 48 h in the general ward or the intermediate care units of four hospitals in Santiago, Chile. The participants in the intervention group will use a tablet with cognitive stimulation software for delirium prevention for five continuous days versus the control group who will use the tablet without the software. We will evaluate the incidence, duration, density of delirium, subsyndromal delirium with the Confusion Assessment Method, cognitive with the Montreal Cognitive Assessment, and functional status with the Functional Independence Measure at discharge. Moreover, we will evaluate the adherence to prevention measures, as well as demographic variables of interest. DISCUSSION: The use of cognitive PREVEDEL software could increase and improve the implementation of non-pharmacological prevention measures for delirium in hospitalised older adults, thus reducing its incidence and contributing to patients and health professionals. TRIAL REGISTRATION: NCT05108207 ClinicalTrials.gov. Registered 4 November 2021.
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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 | 0.001 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".