Low serum triglycerides related to delayed neurocognitive recovery in geriatric oral and maxillofacial surgery patients: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Geriatric patients undergoing oral and maxillofacial surgery are at high risk of delayed neurocognitive recovery (dNCR), yet reliable predictive tools remain unavailable. METHODS: This prospective cohort study (July 2021–January 2025) enrolled patients aged ≥ 65 undergoing elective oral and maxillofacial surgery under general anaesthesia. Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were assessed at baseline and postoperative 1, 3, 7, and 30 days. Serum lipidomics analysis via liquid chromatography-mass spectrometry was performed preoperatively and 24 h postoperatively. The predictive performance of lipid metabolites for dNCR was assessed using receiver operating characteristic curve analysis, with their independent association further evaluated by logistic regression. RESULTS: Among 160 patients, 52 patients (32.5%) developed dNCR. Preoperatively, dNCR patients exhibited significantly lower serum triglyceride (TG), particularly TG(58:7/22:5) (OR = 0.014, 95% CI 0.002 to 0.109, adjusted P < 0.001) and TG(54:2/18:1) (OR = 0.051, 95% CI 0.010 to 0.252, adjusted P = 0.002), which demonstrated strong predictive performance (AUC = 0.86, sensitivity = 0.73, specificity = 0.85). Postoperatively, reduced levels of TG(58:7/22:5) (OR = 0.067, 95% CI 0.015 to 0.309, adjusted P = 0.003) and TG(54:2/18:1) (OR = 0.034, 95% CI 0.006 to 0.176, adjusted P < 0.001) persisted in dNCR patients at 24 h, retaining predictive value for dNCR (AUC = 0.82, sensitivity = 0.75, specificity = 0.78). CONCLUSIONS: Low serum TG(58:7/22:5) and TG(54:2/18:1) are promising biomarkers for early prediction of dNCR, supporting lipidomics-guided perioperative neurocognitive risk stratification.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".