Incidence of post-operative delirium increases as severity of frailty increases
Bibliographic record
Abstract
OBJECTIVE: The surgical population is ageing and often frail. Frailty increases the risk for poor post-operative outcomes such as delirium, which carries significant morbidity, mortality and cost. Frailty is often measured in a binary manner, limiting pre-operative counselling. The goal of this study was to determine the relationship between categorical frailty severity level and post-operative delirium. METHODS: We performed an analysis of a retrospective cohort of older adults from 12 January 2018 to 3 January 2020 admitted to a tertiary medical center for elective surgery. All participants underwent frailty screening prior to inpatient elective surgery with at least two post-operative delirium assessments. Planned ICU admissions were excluded. Procedures were risk-stratified by the Operative Stress Score (OSS). Categorical frailty severity level (Not Frail, Mild, Moderate, and Severe Frailty) was measured using the Edmonton Frail Scale. Delirium was determined using the 4 A's Test and Confusion Assessment Method-Intensive Care Unit. RESULTS: In sum, 324 patients were included. The overall post-operative delirium incidence was 4.6% (15 individuals), which increased significantly as the categorical frailty severity level increased (2% not frail, 6% mild frailty, 23% moderate frailty; P < 0.001) corresponding to increasing odds of delirium (OR 2.57 [0.62, 10.66] mild vs. not frail; OR 12.10 [3.57, 40.99] moderate vs. not frail). CONCLUSIONS: Incidence of post-operative delirium increases as categorical frailty severity level increases. This suggests that frailty severity should be considered when counselling older adults about their risk for post-operative delirium prior to surgery.
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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 | 0.007 |
| 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".