Influence of serum estradiol on serum uric acid level in pre and postmenopausal women.
Bibliographic record
Abstract
Background: Hyperuricemia develops when serum uric acid level exceeds the normal value. Estrogen may influence the level of serum uric acid. Postmenopausal females have a remarkable reduction in its level, so serum estradiol is studied in relation to serum uric acid levels in pre and post-menopausal women. Objective: To find out the relationship of serum Estradiol with serum uric acid level in premenopausal and postmenopausal women in local population. Study Design: Case control study. Setting: This study was conducted in Lady Aitchison Hospital Lahore. Period: March 2017- August 2017. Material and Methods: 134 females were enrolled in total and were grouped in to two. Group A comprised of premenopausal and Group B included postmenopausal females. After complete history and general physical examination, 5 ml venous blood sample under aseptic measures was taken. Serum uric acid was measured by enzymatic and serum estradiol by Enzyme-linked immunosorbent assay method. Results: The mean age of pre and postmenopausal women was 32 and 57 ± 7 years, with significantly lower in premenopause. The mean serum E2 was 91.86 ± 26.71 mg/dL in premenopause and 22.04 ± 9.28 mg/dL in postmenopause, with significantly lower mean in postmenopause. Mean serum uric acid was statistically higher in postmenopause that was 6.04 ± 0.58 mg/dL, when compared to premenopause that was 4.22 ± 0.90 mg/dL. Conclusion: Serum uric acid levels increased due to decreased serum estradiol in postmenopausal women as compared to premenopausal women.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Case-control clinical study of estradiol and uric acid levels.
The study examines estradiol and uric acid levels in menopausal women.
Clinical association of estradiol and uric acid in women; biomedical physiology.
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".