Potential associated factors of postoperative neurocognitive dysfunction in elderly patients: precursor of BDNF and mature BDNF in serum
Bibliographic record
Abstract
BACKGROUND: With the acceleration of population aging in China, the incidence of postoperative neurocognitive dysfunction (PND) will continue to rise. AIM: We aim to evaluate whether the precursor of brain-derived neurotrophic factor (proBDNF) and mature brain-derived neurotrophic factor (mBDNF) are associated with PND in elderly patients. METHODS: Eighty-six patients who were scheduled for elective orthopedic surgery under general anesthesia were ultimately enrolled. Referring to previous studies, the patients were divided into the PND group and the non-PND group with the Montreal Cognitive Assessment (MoCA) scale on postoperative day 7. The Spearman test was used to determine the correlations between mBDNF, proBDNF, and PND. RESULTS: All the participants possessed normal cognitive function before surgery. Compared with those in the nPND group, proBDNF, mBDNF, proBDNF*mBDNF, and MoCA score (all P < 0.001) were lower in the PND group after surgery. The Spearman test showed PND was correlated with postoperative proBDNF (correlation coefficient = -0.391; P = 0.001), mBDNF (correlation coefficient = -0.295; P = 0.006), proBDNF*mBDNF (correlation coefficient = -0.479; P < 0.001), and the ratios of proBDNF (correlation coefficient = -0.388; P < 0.001) and mBDNF (correlation coefficient, -0.375; P < 0.001) from before to after surgery. DISCUSSION: In this study, PND was significantly associated with postoperative proBDNF and mBDNF; the diagnostic value of postoperative mBDNF combined with proBDNF was superior to that of either index alone. CONCLUSIONS: The PND was significantly associated with postoperative proBDNF and mBDNF. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR1800017415, registered date: 07/29/2018), http://www.chictr.org.cn .
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".