The association between preoperative lacunar infarcts and postoperative delirium in elderly patients undergoing major abdominal surgery: a prospective cohort study
Bibliographic record
Abstract
Abstract Objective The primary goal was to investigate whether the presence of preoperative lacunar infarcts (LACI) was associated with postoperative delirium (POD) in elderly patients undergoing elective major abdominal surgery. Design A prospective cohort study. Setting and participants Patients aged ≥ 65 years from a tertiary level A hospital in China. Methods The POD was assessed once daily within the first postoperative 3 days using the Confusion Assessment Method. Neurocognitive tests using the Mini-mental State Examination (MMSE) and the Beijing version of the Montreal Cognitive Assessment scales were carried out within 3 days before surgery and 4–7 days after surgery. Regional cerebral oxygen saturation (rScO2) was recorded in the operating room. Logistic regression analysis was used to evaluate the impact of preoperative LACI on POD and to explore the risk factors for POD. Results A total of 369 participants were analyzed, 161 in the preoperative LACI-positive group (P group), and 208 in the preoperative LACI-negative group (N group), respectively. The incidence of POD was 32.7% in our study. The incidence of POD was significantly higher in the P group than in the N group (39.1 vs 27.9%, risk ratio, 1.66; 95% CI 1.07–2.58; P = 0.022). Furthermore, the P group exhibited lower mean rScO2 values during the procedure (P < 0.001). In exploratory analysis, the advanced age (P = 0.005), sex (P = 0.038), and lower preoperative MMSE score (P = 0.019) were independent risk factors for POD in patients undergoing major abdominal surgery. Conclusions and implications Preoperative LACI was common, and constituted a risk factor for POD in older patients undergoing abdominal surgery. Despite the frequent subclinical nature, the preoperative LACI led to lower mean rScO2 during the procedure. These findings could help early identification of high-risk POD patients.
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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".