Frailty as a mediator of postoperative delirium in older adults undergoing orthopedic surgery: A causal mediation analysis
Bibliographic record
Abstract
Background: Postoperative delirium has a high prevalence in hospitalized older adults. Frail older adults have an increased risk for developing it. Preoperative pain-a common symptom in older adults with orthopedic conditions-shows a connection with frailty through an inflammatory process. However, an association among preoperative pain, frailty, and postoperative delirium remains unexplored among older adults undergoing orthopedic surgery. Objectives: To examine the magnitude, direction, and mediation effect of frailty on the association between preoperative pain and postoperative delirium among older adults undergoing orthopedic surgery. Design: A secondary analysis of a prospective cohort study. Setting and participants: A cohort of 200 older adults (60 years and older) who underwent major orthopedic surgery at a university hospital in Thailand was recruited for the study. Methods: Participants responded to the Demographic and Health Information Form, the Reported Edmonton Frailty Scale-Thai version, and the Thai version of the 4 A's test. Analyses were conducted using a causal mediation analysis with 95 % confidence intervals. Results: We found that 12.5 % of participants developed postoperative delirium. For the direct paths associated with postoperative delirium, statistically significant effects were observed for frailty, preoperative pain, and comorbidity. Considering frailty as a mediator of postoperative delirium, a significant positive indirect effect was identified from preoperative pain. Controlling for age and comorbidities, frailty mediated the association between preoperative pain and postoperative delirium in a statistically significant manner; the average direct effect was 0.014 (95 % confidence interval: 0.008-0.020), the average causal mediating effect was 0.002 (95 % confidence interval: 0.000-0.010), the total effects was 0.017 (95 % confidence interval: 0.010-0.020), and the probability mediation accounted for 14 % (95 % confidence interval: 0.031-0.300). Conclusion: Integrating the concept of age-related decline and frailty assessment may offer opportunities to provide disease-specific care and strengthen precision perioperative care, which ultimately enhance quality of life in older adults undergoing orthopedic surgery.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".