The Effect of Gabapentin on Intensity and Duration of Hot Flashes in Postmenopausal Women: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Menopause is the stage of time in which the menstruation stops following the loss of ovarian activity. The purpose of this study was to find out the effectiveness of gabapentin on hot flashes in postmenopausal women. MATERIALS & METHODS: A randomized controlled trial from Feb 2010 to 2011 was conducted. Sixty postmenopausal women who were referred to obstetrics and gynecology ward of two educational hospitals were recruited and divided into two groups (intervention and control). Intervention group received 300 mg gabapentin three times a day for three months, while control group received placebo. The Intensity and duration of hot flashes in women scored and recorded using visual analog scale. Independent, Paired t-test and chi-square test were used for analyzing data. RESULTS: Intensity of hot flashes in the beginning of research in the intervention group was significantly different with the first, second and third follow-up visit (P<0.05). Also at the end of intervention a significant difference between intervention and control groups were observed regarding the intensity, frequency and duration of hot flashes (P<0.05 and P=0.01 respectively). CONCLUSION: According to the findings of this study; it appears that the use of gabapentin could decrease the intensity, duration and frequency of hot flashes in postmenopausal women. For postmenopausal women who hormone therapy is contraindicated, gabapentine could be an acceptable alternative.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".