Prevalence and risk factors of early postoperative seizures in patients with glioma: A protocol for meta-analysis and systematic review
Bibliographic record
Abstract
INTRODUCTION: Early postoperative seizures has been the most common clinical expression in gliomas; however, the incidence and risk factors for early postoperative seizures in gliomas are more controversial. This protocol describes a systematic review and meta-analysis to clarify the prevalence and risk factors of early postoperative seizures in patients with glioma. METHODS AND ANALYSIS: Searches will be conducted on CNKI, WanFang, VIP, PubMed, Embase, Cochrane Library databases and Web of Science for the period from database inception to December 31st, 2023. Case-control and cohort studies of the incidence and risk factors for early postoperative seizures in all gliomas will be included. The primary outcome will be incidence, risk factors. Newcastle-Ottawa Scale was used for quality evaluation. Review of article screening, extracting data and risk of bias assessment will be repeated by two independent reviewers. RESULT: This study will provide evidence for the risk factors and incidence of early postoperative seizures in patients with glioma. CONCLUSION: Our study will provide evidence for the prevention of early postoperative seizures in glioma patients. TRAIL REGISTRATION: This protocol was registered in PROSPERO and registration number is CRD42023415658.
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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 | 0.056 | 0.111 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.003 |
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".