The risk factors of postoperative cognitive dysfunction in patients undergoing carotid endarterectomy: an updated meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The purpose of the current meta-analysis was to determine the incidence and risk factors to provide a scientific basis for prevention and treatment of postoperative cognitive dysfunction (POCD) after carotid endarterectomy (CEA). METHODS: Relevant articles published before October 2022 were searched from Pubmed/MEDLINE, Cochrane and Embase databases. The outcomes were the incidence and risk factors for POCD. A random-effects model was applied to estimate the overall odds ratios (ORs) and mean differences (MDs) for all risk factors through STATA 14.0 and RevMan 5.4. The quality of eligible studies was evaluated by Newcastle-Ottawa Scale (NOS) as previously described. RESULTS: A total of 22 articles involving 3459 CEA patients were finally identified. The weighted mean incidence of POCD was 19% (95% confidence intervals (95% CI) 0.16-0.24, P < 0.001). Of the 16 identified risk factors, hyperperfusion (OR: 0.54, 95% CI 0.41-0.71) and degree of internal carotid artery (ICA) stenosis (OR: 5.06, 95% CI 0.86-9.27) were the potential risk factors of POCD, whereas patients taking statins preoperative had a lower risk of POCD (OR: 0.54, 95% CI 0.41-0.71). Subgroup analysis revealed that the risk of POCD at 1 month after CEA was higher in patients with diabetes (OR: 1.70, 95% CI 1.07-2.71). CONCLUSION: The risk factors of POCD were hyperperfusion and degree of ICA stenosis, while diabetes could significantly increase the incidence of POCD at 1 month after surgery. Additionally, preoperative statin use could be a protective factor for POCD following CEA.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.057 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".