Implementing the best available evidence in early delirium identification in elderly hip surgery patients
Bibliographic record
Abstract
AIMS: Delirium is a frequent complication in the surgical experience of elderly hip surgery patients. Its impact can be severe and may even include death. Implementation of a delirium predictor tool might focus attention on early recognition of delirium, thereby potentially decreasing its impact. A related aim is to evaluate best practices in implementation strategies in this project. METHODS: After an exhaustive search of the literature, no consensus was found regarding delirium predictors for the elderly hip surgery patient. A local research study was implemented to determine factors that may predict delirium in this population. With evidence secured, a multidisciplinary implementation project augmented by ongoing audit was instituted. A variety of social diffusion and education tools were used. Implementation was guided by the use of the Promoting Action on Research Implementation in Health Services framework assessment tool and the Alberta Context Tool, as well as traditional performance improvement tools, such as fishbone charting. Audit identified the rate of use of the predictor tool and pre- and post-rates of delirium. This project was part of the Joanna Briggs Institute Signature Project, an implementation project consisting of six teams, each representing a different organisation. This overall project was supported by experts in the field of translation and implementation science internationally. RESULTS: Initial compliance to the use of the predictor tool was assessed at 54% within 3 months of implementation and increased to 56% in the ensuing months. Before the study use of the predictor tool, the delirium rate was 10.4% (12 of 115 patients). An interim analysis 4 months after implementation identified a 20% delirium rate (18 of 70 patients) and an updated analysis 8 months into the project showed a 16.3% delirium rate. Delirium predictor tool use was associated with a lower delirium rate (9/76, 11.84%) than no delirium predictor tool (13/60, 21.67%), but the difference was not statistically significant with a sample size of 133 (P = 0.122). CONCLUSIONS: The delirium predictor tool shows promise as a prompt for best practices in prevention of delirium. This study showed a change in delirium rates as a result of its use. Although the results were not statistically significant, they may be clinically meaningful. Comprehensive assessment and implementation planning by a multidisciplinary team contributed to only 56% compliance in use. Despite this low rate, delirium identification rates were higher.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".