Research Review: Are sampling biases masking long‐term effects of hormonal contraceptive use in adolescence on risk for depression?
Bibliographic record
Abstract
BACKGROUND: Growing evidence suggests that the use of hormonal contraceptives (HCs) during adolescence may be linked to an increased risk for depression. This review examines major inconsistencies that have been reported regarding this relationship, and in particular, how the common practice of combining 'never users' and 'former users' of HCs in analyses obscures patterns that are detectable when these groups are analyzed separately. METHODS: A review was conducted of research examining the relationship between HC use and depression to determine what data-analytic choices were commonly made by individual researchers. Specifically, we assessed whether the past history of HC use had been accounted for in each reported analysis. RESULTS: The majority of papers published between 2013 and 2022 did not account for the former use of HCs. These papers reported mixed findings regarding the relationship between HC use and depression. In contrast, the subset of papers that did account for former use of HCs, or otherwise explicitly addressed common biases affecting the interpretation of observational data, revealed a more consistent relationship between HC use and depression, particularly for those who began using HCs during adolescence. CONCLUSION: We conclude that there is consistent evidence of a relationship between adolescent HC use and long-term risk for depression and offer several recommendations to help ensure that future work in this area will yield consistent, interpretable findings. Although this paper focuses primarily on HCs and depression, many of the analytical approaches and recommendations outlined within it are also relevant to research on the side effects of other drugs and medications.
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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 | 0.071 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".