Risk factors for delayed neurocognitive recovery in elderly patients undergoing thoracic surgery
Bibliographic record
Abstract
BACKGROUND: To investigate the risk factors for delayed neurocognitive recovery in elderly patients undergoing thoracic surgery. METHODS: A total of 215 elderly patients who underwent thoracic surgery between May 2022 and October 2022 were recruited in this prospective observational study. Cognitive function was tested by MoCA tests that were performed by the same trained physician before surgery, on postoperative day 4 (POD4), and on postoperative day 30 (POD30). Univariate and multivariate logistic regression models were used to analyze the risk factors for DNR. RESULTS: A total of 154 patients (55.8% men) with an average age of 67.99 ± 3.88 years were finally included. Patients had an average preoperative MoCA score of 24.68 ± 2.75. On the 30th day after surgery, 26 (16.88%) patients had delayed postoperative cognitive recovery, and 128 (83.12%) had postoperative cognitive function recovery. Diabetes mellitus (OR = 6.508 [2.049-20.664], P = 0.001), perioperative inadvertent hypothermia (< 35℃) (OR = 5.688 [1.693-19.109], P = 0.005), history of cerebrovascular events (OR = 10.211 [2.842-36.688], P < 0.001), and VICA (sevoflurane combined with propofol anesthesia) (OR = 5.306 [1.272-22.138], P = 0.022) resulted as independent risk factors of delayed neurocognitive recovery. On the POD4, DNR was found in 61 cases (39.6%), and age ≥ 70 years (OR = 2.311 [1.096-4.876], P = 0.028) and preoperative NLR ≥ 2.5 (OR = 0.428 [0.188-0.975], P = 0.043) were identified as independent risk factors. CONCLUSIONS: The risk factors for delayed neurocognitive recovery in elderly patients undergoing thoracic surgery include diabetes, perioperative inadvertent hypothermia (< 35℃), VICA (sevoflurane combined with propofol anesthesia), and history of cerebrovascular events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".