Muscular Hypertrophic Effects of Oral Contraceptive Consumption in Women
Bibliographic record
Abstract
Hormones have several actions in the body; among their effects on effector organs, they can control the volume of skeletal muscle. The excess of hormones, in this case exogenous ones, could be altering the effects of physical exercise on skeletal muscle hypertrophy, especially after the consumption of contraceptives. Thus, we have a general objective in this work to evaluate the effects of contraceptive consumption on hypertrophy in women. We sought an integrative literature review with studies within 2001-2021, using the PubMed and Scielo databases. Seven articles were selected for a complete and coherent reading for the development of this review. Given the studies analyzed in the present study, it was possible to observe that the effects of oral contraceptives may indeed influence muscle hypertrophy. However, it can assist in preserving soft tissues, avoiding the looseness of the anterior cruciate ligament (ACL). For individuals aiming to enhance lean muscle mass, particularly in high-performance contexts, it is advisable to consider alternative contraceptive methods that do not disrupt the natural hormonal system. In conclusion, the OC user group did not obtain significant gains in lean mass, but their levels of dehydroepiandrosterone (DHEA), sex hormone agglobulinal ligand (SHBG), and insulin-like growth factor (IGF1) decreased, with increasing levels of only cortisol. Thinking of high performance to increase lean muscle mass, another means of contraceptives that do not affect the physiological hormonal system should be used.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".