Psychological side-effects of clomiphene citrate and human menopausal gonadotrophin
Bibliographic record
Abstract
OBJECTIVE: This study evaluated the psychological side-effects of clomiphene citrate (CC) and hMG in women undergoing fertility treatment. METHOD: This study was a cross-sectional, self-report survey of 454 women at various stages of treatment for infertility. At the time of study, 139 women had not taken fertility drugs and 315 women had taken one or more cycles of CC or hMG. All subjects were asked to complete the State-Trait Anxiety Inventory (STAI). Women taking CC or hMG were also asked to complete a self-administered questionnaire on the side-effects of their medications. RESULT(S): In the CC group (n = 162) and hMG group (n = 153), 77.8% (126 of 162) and 94.8% (145 of 153) reported at least one side-effect, respectively. Irritability, mood swings, feeling down, and bloating had high frequencies in both CC and hMG groups, with a higher mean number of side effects reported in the hMG group (4.4 +/- 3.7 for the CC group and 6.8 +/- 3.7 for the hMG group, p < 0.001). There was no significant difference among the CC, hMG and no medication groups for mean state and trait anxiety scores. However, there were significant differences among the three side-effect groups (those who reported 1 to 4, 5 to 7, and more than 7 side-effects) for the mean scores of state (df = 2, F = 8.7, p < 0.001) and trait (df = 2, F = 11.9, p < 0.001) anxiety in women taking fertility drugs. CONCLUSION(S): Women taking CC or hMG reported high frequencies of psychological side-effects, and should be advised of these before treatment.
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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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".