Preconception cardiometabolic health in patients seeking fertility services: protocol for a single-site, prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Weight loss is often recommended as the primary intervention for infertility in individuals with high body mass index. However, focusing on body mass index might overlook other important factors like cardiometabolic health. This study aims to examine cardiometabolic health in patients seeking fertility treatment and its impact on reproductive outcomes. METHODS AND ANALYSIS: A cross-sectional analysis of 800 systematically selected participants (400 couples) will be completed on a single visit to the study site. This session will involve taking blood samples to examine metabolic biomarkers, the completion of questionnaires assessing preconception health factors and an exercise 'step test' to assess cardiorespiratory fitness. Metabolic panels will be compared with target values and, where available, normative population data. Fitness data will be transformed into normative percentile values based on the participant's age and sex. Patients will be followed for 2 years to allow yearly data collection related to conception, gestation and parturition. Associations between cardiometabolic health during the preconception phase and reproductive outcomes will be examined. ETHICS AND DISSEMINATION: The Newfoundland and Labrador Health Research Ethics Board has provided ethical approval for this study (HREB #20230825). Each patient will be required to give written consent prior to any data collection. We will share study findings at conferences and submit manuscripts to peer-reviewed journals. Additionally, we will create knowledge translation presentations for Newfoundland and Labrador Fertility Services and Family Practice Clinics.
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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 | 0.042 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".