Prevalence of Major Neurocognitive Disorder in Patients Admitted with Hip Fractures and the Associated Postoperative Delirium and Other Surgical Outcomes - A Prospective Cohort Study (Hippod Study)
Bibliographic record
Abstract
Introduction: Major Neurocognitive disorder (NCD) and hip fractures are two serious problems in aging population. They are associated with morbidities including postoperative delirium (POD). We aimed to look at the prevalence of major NCD in hip fractures in our locality and their association with POD. Methods: It was a prospective cohort study. Patients ≥ 65 years old with hip fractures communicable in Cantonese were included. They were screened with Hong Kong Montreal Cognitive Assessment 5-min protocol before operation. They were reviewed postoperatively with 3-Minute Diagnostic Interview for Confusion Assessment Method to assess the presence of POD. The primary outcome was the incidence of delirium between patients with or without preoperative major NCD. Secondary outcomes including surgical outcomes and length of hospital stay were investigated. Results: The study was conducted between November 2020 and March 2021. One hundred ninety-two patients were screened, and 122 patients were included. Among the 192 patients screened, 97 (50.5%) were found to have major NCD. POD was found in 68.1% and 21.3% of patients with or without major NCD respectively. (p<0.001, odds ratio 4.857 (95% CI 2.046-11.531)). Total length of stay in hospital was longer when POD developed (p<0.05). Conclusion: High prevalence of major NCD was found in geriatric hip fracture patients. Pre-existing major NCD was an independent risk factor in developing POD. Both major NCD and POD were prevalent but not sufficiently addressed in our locality. A multidisciplinary collaboration between anesthetists, geriatricians and allied health workers may help to prevent POD and reduce its severity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".