Sex is a critical factor in the timing of surgical intervention in men and women with severe carotid artery disease: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Stroke is a leading cause of mortality worldwide among men and women. Sex differences exist in stroke incidence, mortality, long-term functional outcomes, and treatment responses. Timing of carotid surgical intervention is essential in the prevention of strokes, particularly among women. However, it remains unclear whether sex is a critical factor that influences surgical wait times. In this protocol we outlined a systematic review and meta-analysis regarding sex differences in the timing of surgical intervention among men and women with severe carotid artery disease, as well as secondary analyses assessing the impact of delayed intervention on perioperative and postoperative clinical outcomes.Various electronic databases will be searched: Medline, Embase, The Cochrane Library, PubMed, CINAHL Plus, Scopus, grey literature, and trial registries. Search strategies will be designed to identify human (≥18 years) controlled trials, cohort studies, case-control studies, and cross-sectional studies concerning “sex differences in the timing of surgical intervention in men and women with severe carotid artery disease.” A preliminary search strategy was developed for Medline (1946 to August 3rd, 2020). For primary outcomes, data must involve timing spanning from symptom onset to surgical intervention in symptomatic individuals, or timing spanning from first medical contact to surgical intervention in asymptomatic individuals. Secondary outcomes include effect estimates for peri-operative and post-operative cardiovascular (including cerebrovascular) morbidity and mortality, based on timing of intervention. Pooled analyses will be conducted using the random-effects model. Publication bias will be assessed by visual inspection of funnel plots and by Begg’s and Egger’s statistical tests. Between-studies heterogeneity will be measured using the I2 test (P<0.10). Sources of heterogeneity will be explored by sensitivity, subgroup, and meta-regression analyses. Findings will be shared through scientific conferences and societies, social media, and consumer advocacy groups. Results will be used to inform current guidelines for carotid disease management and stroke prevention in men and women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.088 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.038 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 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".