Risk factors for anterior communicating artery aneurysm rupture
Bibliographic record
Abstract
BACKGROUND: Although the research on the risk factors of anterior communicating artery (AComA) aneurysm has made great progress, the independent effect of each risk factor on the rupture of AComA aneurysm is controversial among different studies. We will perform a protocol for systematic review and meta-analysis to investigate risk factors for AComA aneurysm rupture and quantify their independent effects. METHODS: A systematic search according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guidelines in PubMed, Embase, and the Cochrane Library databases was conducted from inception to August 31, 2021 for published studies concerning risk factors for AComA aneurysm rupture. In the absence of statistical heterogeneity (ie, P > .10 and I2 < 50%), we will use a fixed-effects model to pool the results across sufficient studies. Otherwise, we will present the results employing the random-effects model. Quality assessment of the included studies will be evaluated using the Newcastle-Ottawa Scale. Statistical analyses will be performed using Stata16 (Stata Corporation, College Station, TX, USA) software. RESULTS: The findings of this study will be submitted to peer-reviewed journals for publication. CONCLUSION: This systematic review will provide evidence to determine the risk factors that affect the rupture of the AComA aneurysm and quantify their independent effects. ETHICS AND DISSEMINATION: Since the proposed study uses pre-published data, ethical approval is not required. REVIEW REGISTRATION NUMBER: CRD42021284262. (https://www.crd.york.ac.uk/PROSPERO/).
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".