Frailty as a predictor of neurosurgical outcomes in brain tumor patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Patients with frailty are at a high risk of poor health outcomes, and frailty has been explored as a predictor of adverse events, such as perioperative complications, readmissions, falls, disability, and mortality in the neurosurgical literature. However, the precise relationship between frailty and neurosurgical outcomes in patients with brain tumor has not been established, and thus evidence-based advancements in neurosurgical management. The objectives of this study are to describe existing evidence and conduct the first systematic review and meta-analysis of the relationship between frailty and neurosurgical outcomes among brain tumor patients. Methods Seven English databases and four Chinese databases were searched to identify neurosurgical outcomes and the prevalence of frailty among patients with a brain tumor, with no restrictions on the publication period. According to the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines, two independent reviewers employed the Newcastle–Ottawa scale in cohort studies and JBI Critical Appraisal Checklist for Cross-sectional Studies to evaluate the methodological quality of each study. Then random-effects or fixed-effects meta-analysis was used in combining odds ratio (OR) or hazard ratio (RR) for the categorical data and continuous data of neurosurgical outcomes. The primary outcomes are mortality and postoperative complications, and secondary outcomes include readmission, discharge disposition, length of stay (LOS), and hospitalization costs. Results A total of 13 papers were included in the systematic review, and the prevalence of frailty ranged from 1.48 to 57%. Frailty was significantly associated with increased risk of mortality (OR = 1.63; CI = 1.33–1.98; p < 0.001), postoperative complications (OR = 1.48; CI = 1.40–1.55; p < 0.001; I 2 = 33%), nonroutine discharge disposition to a facility other than home (OR = 1.72; CI = 1.41–2.11; p < 0.001), prolonged LOS (OR = 1.25; CI = 1.09–1.43; p = 0.001), and high hospitalization costs among brain tumor patients. However, frailty was not independently associated with readmission (OR = 0.99; CI = 0.96–1.03; p = 0.74). Conclusion Frailty is an independent predictor of mortality, postoperative complications, nonroutine discharge disposition, LOS, and hospitalization costs among brain tumor patients. In addition, frailty plays a significant potential role in risk stratification, preoperative shared decision making, and perioperative management. Systematic review registration PROSPERO CRD42021248424
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".