Serum Creatine Kinase Activity Varies with Ovulatory Status in Regularly Exercising, Premenopausal Women
Bibliographic record
Abstract
BACKGROUND/AIMS: The clinical complications associated with an unopposed estrogen environment and luteal phase defects observed in exercising women prompted the examination of the relationship of exercise and endogenous ovarian steroids with serum creatine kinase (CK) activity. METHODS: Subjects (n = 34) were classified into three groups according to their exercise and menstrual status, sedentary and exercising ovulatory groups (SedOvul, ExOvul), and an exercising amenorrheic group (ExAmen). Daily urine samples were collected to assess urinary ovarian steroid exposure and menstrual status. Serum CK activity was assayed in each menstrual cycle of all subjects. RESULTS: Exercise increased serum CK activity in all exercising subjects (p < 0.01), but the increase was greater in amenorrheic women compared to ovulatory women (SedOvul: 33.0 +/- 3.4; ExOvul: 43.7 +/- 4.1; ExAmen: 54.4 +/- 3.6, p < 0.05). When the ovulatory women were further divided into those with normal steroid production (ExOvul subgroup) and those with a suppressed progesterone luteal phase environment (ExLPD), both the ExOvul (51.9 +/- 5.4 IU/l) subgroup and ExAmen group had higher serum CK activity (p < 0.05) than the ExLPD (36.6 +/- 5.2 IU/l) subjects or the sedentary controls. CONCLUSIONS: These data demonstrate the complex association between ovarian hormone status and the normal serum CK response to regular mechanical stress imposed by chronic exercise training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".