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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".