Establishing a Comprehensive Framework for Future Explorations: An Endometriosis and Cardiovascular Disease Literature Review
Bibliographic record
Abstract
Endometriosis is one of the most prominent gynecological disorders often associated with several cardiovascular repercussions. Although no conclusive mechanism has been found, previous literature indicates potential links between endometriosis and atherosclerosis, a vital indicator of cardiovascular disease (CVD). However, with the majority of previous studies overlooking the impact of critical confounding variables and testing for only certain biomarkers, a strong argument towards a link cannot be made. Existing literature was thoroughly analyzed to identify major confounding variables that were unaccounted for to compile a list of vital biomarkers indicative of CVD in women with endometriosis. The stage and severity of the disease, surgery, hormone therapy, and presence of endometriosis in the control group were found to be major confounding variables that should be statistically accounted for. From previous literature, biomarkers that were shown to be highly indicative of CVD included lipid profile, arterial stiffness measures, as well as additional measures of vascular function and structure. Encapsulating vital confounding variables and biomarkers, a comprehensive framework was established for a longitudinal study design. This paper provides a narrative review of the common weaknesses and limitations of past investigations exploring the link between endometriosis and CVD and suggests methods to overcome these considerations. Although existing literature has significantly contributed to the surface-level understanding of the link between endometriosis and CVD, knowledge gaps persist. As a result, repercussions are experienced by women with endometriosis worldwide. To ensure better healthcare for women with endometriosis, greater CVD intervention and prevention is critical. Through the holistic longitudinal study design proposed, improved treatment plans considering the potential CVD risks that women with endometriosis are at a greater likelihood of developing can be implemented.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".