127DELIRIUM PREVENTION IN ELECTIVE ORTHOPAEDIC SURGERY: A QUALITY IMPROVEMENT PROJECT
Bibliographic record
Abstract
Introduction: Post-operative delirium is a serious complication in elderly patients. This quality improvement project was aimed reducing delirium and length of stay (LOS) in elective orthopaedic patients at Chesterfield Royal Hospital. Methods: Funding was provided by East Midlands Academic Health Science network for a Delirium Nurse Practitioner (DNP). The DNP interviewed 237 elective hip and knee replacement patients between February and August 2017. An Edmonton Frail Score (EFS), 4AT, Montreal Cognitive Assessment (MOCA), Delirium Elderly at Risk (DEAR) score and medical history was completed for each patient. Patients were provided with delirium counselling, written information on delirium and an individualised care plan to be used during their admission. Patient’s with a DEAR>2, EFS > 7, or unstable co-morbidity were referred for pre-operative orthogeriatric assessment. During admission Delirium Observation Scoring (DOS) was implemented. Data on age, sex, LOS and inpatient delirium diagnosis was also collected on 167 patients (no-intervention group) who received hip or knee replacement surgery. Results: Chi square analysis for MOCA was significant at <0.05 for the development of delirium: other scores used were not significant. The intervention commenced in February 2017 and from this point analysis of LOS has shown a reduction in the median LOS: 0.935 days for knee patients and 0.625 for hips. There was no significant difference in the development of delirium between the intervention and no-intervention groups. The median age of the intervention group (73 years, IQR: 67–79) was significantly greater (P < 0.01) than the no-intervention group (67 years IQR 58–75). Conclusion: The project was well received by patients with 78% stating that the information they received about delirium was ‘definitely useful’. Analysis of the LOS data has also shown that the intervention is contributing to a reduction in the median LOS. It is possible that delirium was under diagnosed in the no-intervention group as implementation of DOS may have resulted in higher rates of delirium diagnosis in the intervention group. It could also be suggested that despite accepting older patients for elective hip and knee replacement surgery the incidence of delirium did not increase with the intervention in place. Further work to assess post-operative delirium screening tools and the impact of the intervention in case controlled groups is required.
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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 | 0.000 |
| 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".