Frailty as a Predictor of Neurosurgical Outcomes in Brain Tumor Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Purpose The object of this study is to describe the existing evidence and completed the first systematic review meta-analysis between frailty and neurosurgical outcomes in brain tumor patients. The primary outcome is mortality and postoperative complications, the second outcomes including readmission rate, discharge disposition, length of stay (LOS) and hospitalization costs.Methods Seven English databases and four Chinese databases were searched to identify the neurosurgical outcomes and frailty among patients with brain tumor. With no restrictions on the publication period. According to the JBI manual for evidence synthesis and the PRISMA guidelines, two independent reviewers applied the Newcastle-Ottawa Scale (NOS) for cohort studies, the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Cross-Sectional Studies to evaluate the methodological quality of each study.Results 13 papers included in the systematic review and prevalence of frailty ranged from 1.48% to 57%. Frailty is significantly associated with increased the 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; I2=33%), non-routine discharge position than home (OR,1.72; CI,1.41-2.11; P＜0.001), prolonged LOS in brain tumor patients (OR=1.25; CI=1.09-1.43; P=0.001) and higher hospitalization costs in brain tumor patients. But 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, non-routine discharge position rate, LOS and hospitalization costs in brain tumor patients. Besides frailty has a significant potential role in risk stratification, preoperative shared decision-making and perioperative management.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".