Neurocognitive Outcomes After Extracranial Surgery and General Anesthesia in Patients with a History of Mild-to-Moderate Traumatic Brain Injury: Systemic Review and Meta-Analysis
Bibliographic record
Abstract
Accelerated neurocognitive decline associated with surgeries under general anesthesia (GA), a phenomenon referred to as postoperative neurocognitive disorder (PND), is a significant public health concern. It not only poses inherent risks but may also contribute to the development of other neurodegenerative disorders. We systematically searched five databases for studies examining cognitive function in patients with mild-to-moderate TBI with (participant) or without (control) subsequent extracranial surgeries/GA. A random effects model was applied to calculate mean differences (MDs) and 95% confidence intervals (CIs). Five outcomes were analyzed post hoc: trail-making tests A and B (TMT-A/B), Glasgow Outcome Scale–Extended (GOSE), and length of stay (LOS) in intensive care units (ICUs) and hospitals. Five studies met the criteria for our meta-analysis. Patients with a history of mild-to-moderate TBI who underwent extracranial surgeries/GA exhibited worse outcomes in TMT-A [MD = 2.04; CI 0.38–3.70; p = 0.016] and TMT-B [MD = 16.59; CI 9.58–23.60; p < 0.001]. Differences in the ICU and hospital LOS and GOSE between the study groups were insignificant. Our results suggest that extracranial surgeries/GA may worsen neurocognitive outcomes without affecting functional recovery in mild-to-moderate TBI patients. Given the limited number of studies identified and the high incidence of TBI, more research on PND in TBI patients is warranted.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".