Frailty Indices in Patients Undergoing Functional Neurosurgical Procedures: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Functional neurosurgery covers a wide array of neurological disorders with an equally vast array of treatment modalities, including neuromodulation, decompressive, and ablative therapies for disparate pathologies such as pain, neuromodulation, disconnection, and refractory epilepsy. One of the most common functional treatments is deep brain stimulation for movement disorders and select psychiatric diseases. Functional neurosurgery treats patients with reduced quality of life from pathological neuronal pathways. Optimal patient selection by preoperatively identifying high-risk patients is critical for avoiding as many operative complications as possible, in addition to managing complications better once they occur. Frailty indices have demonstrated superior discrimination in predicting adverse postoperative outcomes across the spectrum of neurosurgical subspecialties when compared to increasing patient age. This systematic review describes multiple different frailty indices utilized by patients undergoing functional neurosurgery procedures. METHODS: A systematic review of literature was performed using PubMed. The Newcastle Ottawa Scale (NOS) was used to assess for risk of bias and studies with NOS >6 were considered high-quality. An initial search identified 541 articles through our search strategy and, after screening and review, five met criteria for inclusion The 5-factor modified frailty index (mFI-5) and Risk Analysis Index (RAI) were most frequently utilized (n = 5). One study utilized single-hospital databases in contrast to the nationwide databases utilized by the other four studies. RESULTS: RAI was found to have superior predictive ability as frailty metric when compared to the mFI-5. All five studies were considered high-quality based on the NOS. Frailty indices have demonstrated the ability to predict adverse outcomes in patients undergoing procedures from across the spectrum of neurosurgical subspecialties. CONCLUSION: Our review identified articles that utilized frailty indices in predicting outcomes among patients undergoing functional neurosurgery procedures.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".