Detecting Delirium in Hospitalized Elderly Patients: A Review of Practice Compliance
Bibliographic record
Abstract
Background: The Ontario Senior Friendly Hospital Strategy recognizes delirium prevention and management as a top priority and recommends implementation of delirium screening as well as management protocols. This strategy proposes that hospitals monitor 2 specific indicators: (1) rate of baseline delirium screening and (2) rate of hospital-acquired delirium. Objective: To (1) determine compliance with the Ontario Senior Friendly Hospital Strategy indicators; (2) describe the use of pharmacological and nonpharmacological interventions for management of delirious patients; and (3) identify predictors of screening compliance. Methods: We conducted a retrospective review of patients aged ≥65 years admitted to 4 different inpatient units for ≥48 hours. Data were extracted for 7 two-month time blocks chosen between September 2010 and October 2013, following the implementation of various geriatric and delirium related initiatives at the hospital. Results: A total of 786 patients met study inclusion criteria. Overall, 68.2% had baseline delirium screening (indicator 1), with screening rates increasing over time ( P < .001). Inpatient unit and year of study were both statistically significant predictors of delirium screening. Among those screened, the overall rate of hospital-acquired delirium was 17.2% (indicator 2). Early mobilization and device removal were the most common nonpharmacological interventions, while initiation of an antipsychotic and discontinuation of benzodiazepines were the most common pharmacological interventions. Conclusions: Although the rates of baseline delirium screening have significantly increased over the sampled time period, rates are still below the averages referenced in other literature. Our study suggests we need additional efforts to improve compliance with delirium screening in our institution.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| 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".