Development of a delirium risk screening tool for long‐term care facilities
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to develop a delirium risk screening tool for use in long-term care (LTC) facilities. METHODS: The sample comprised residents aged 65 years and over of seven LTC facilities in Montreal and Quebec City, Canada, admitted for LTC. Primary analyses were conducted among residents without delirium at baseline. Incident delirium was diagnosed using multiple data sources during the 6-month follow-up. Risk factors, all measured at or prior to baseline, included the following six groups: sociodemographic, medical, cognitive status, physical function, agitated behavior, and symptoms of depression. Variables were analyzed individually and by group using Cox regression models. Clinical judgment was used to select the most feasible among similarly performing factors. RESULTS: The cohort comprised 206 residents without delirium at baseline; 69 cases of incident delirium were observed (rate 7.6 per 100 person weeks). The best-performing screening tool comprised five items, with an overall area under the curve of 0.82 (95% CI 0.76, 0.88). These items included brief measures of cognitive status, physical function, behavioral, and emotional problems. Using cut-points of 2 (or 3) over 5, the scale has a sensitivity of 90% (63%), specificity of 59% (85%), and positive predictive value of 52% (66%). CONCLUSIONS: This brief screening tool allows nurses to identify LTC residents at increased risk for delirium. These residents can be targeted for closer monitoring and preventive interventions.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".