Continuation versus discontinuation of aspirin-based antiplatelet therapy for perioperative bleeding and ischaemic events in adults undergoing neurosurgery: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Antiplatelet therapy is commonly used in primary or secondary prevention of atherosclerotic and thrombotic diseases, such as coronary artery disease, transient ischaemic attack or stroke. Recent studies noted that antiplatelet therapy should be continued perioperatively in patients at high risk of thrombosis and low bleeding risk in orthopaedic, spinal or urological surgery. However, evidence in neurosurgery is lacking. Thus, we aim to conduct a systematic review and meta-analysis to assess whether the continuous use of antiplatelet drugs in neurosurgery increases the risk of perioperative bleeding. METHODS AND ANALYSIS: We will search PubMed, Cochrane Central Register of Controlled Trials and Embase using a strategy that combines the terms aspirin, bleeding/ischaemic and neurosurgery. Two reviewers will independently screen all identified abstracts for eligibility and evaluate the risk of bias of the included studies using the Cochrane risk of bias tool for randomised controlled studies and the Newcastle-Ottawa Scale for observational studies (including cohort studies, case-control studies, case series). Discrepancies will be resolved by consultation with a third researcher. We will conduct a systematic review and meta-analysis. If evidence suggests moderate statistical or clinical heterogeneity, we plan to investigate this heterogeneity by performing subgroup analyses and sensitivity analysis. ETHICS AND DISSEMINATION: No ethics approval will be sought as no original data will be collected for this review. Findings will be disseminated through peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42020202590.
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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 | 0.058 | 0.075 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.005 |
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".