Impact of patient gender on surgical outcomes of infective endocarditis in adults: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: Infective endocarditis is a rare but severe disease affecting 3-10 per 100 000 each year associated with a mortality of 25%. However, it is thought to affect females more severely than men, despite lower incidence. However, reasons for this are unknown, and there is controversy surrounding evidence for differences in surgical outcomes for infective endocarditis. Thus, a systematic review and meta-analysis is warranted to elucidate differences in outcomes by gender. METHODS AND ANALYSIS: This systematic review protocol has been developed in accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines. A systematic search including synonyms of the terms 'infective endocarditis', 'cardiac surgery' and 'sex', was carried on MEDLINE, Embase and Scopus databases (full search available in the Supplementary Material) to identify relevant studies. The Cochrane Risk of Bias 2 tool and Newcastle-Ottawa Scale will be used to assess the quality of the available studies and risk of bias. Studies will be screened using predetermined inclusion and exclusion criteria. Data will be summarized narratively and in tabular forms. A pairwise meta-analysis will be carried out with a random effects model to examine differences in mortality and postoperative complications between males and females. DISCUSSION: The findings will elucidate the influence of gender on surgical outcomes for infective endocarditis, informing evidence-based interventions and emphasizing the need for equitable surgical care. By identifying risk factors specific to women, this study aims to improve management strategies and outcomes for female patients with infective endocarditis. Results will be disseminated via peer-reviewed publications and relevant conferences.
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 | 0.051 | 0.078 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 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".