Cohort studies in the context of obstetric and gynecologic research: a methodologic overview
Bibliographic record
Abstract
Observational cohort studies represent one of the most powerful designs in epidemiology. They are also the basis of evidence in many areas of obstetric and gynecologic research, given that randomization of women, couples or pregnancies is often impossible or unethical. Indeed, well-conceived cohort studies have led to a better understanding of many important clinical and public health questions over time, including the impact of different exposures on perinatal and pediatric outcomes in pregnant women and their children. In this paper, we describe the main features, challenges, and limitations of cohort studies in the context of obstetric and gynecologic research. As with all epidemiologic studies, cohort studies present numerous challenges and are vulnerable to bias. However, as we describe throughout this review, careful design - from formulating the study question to planning statistical analysis - can reduce the potential for bias. When possible, we also provide examples from the gynecological and obstetrical literature to illustrate the epidemiological challenge and suggest specific readings.
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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 | 0.084 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".