Increased blood glucose level following hysterectomy among reproductive women in India
Bibliographic record
Abstract
BACKGROUND: In recent years, the hysterectomy, a surgical removal of the uterus, has received increased attention in health policy debates in India. The trigger for this was a series of media reports that highlighted an unusual surge in the number of women undergoing hysterectomies with a significant number of cases involving young and early menopausal women from low-income families. When menopause occurs as a result of hysterectomy, then the hormones such as estrogen and progesterone affect how the body cells respond to insulin. To date, we have not come across a national study following blood glucose levels among women who undergo a hysterectomy. METHODS: The study used the Indian fourth round of National Family Health Survey data, which is a cross-sectional nationally representative sample of 699,686 women in the age group 15-49 years and conducted during 2015-16. Bivariate and multivariate logistic regressions were used to examine the effect of hysterectomy on blood glucose level of > 140 mg/dl among women of reproductive age groups. RESULTS: The blood glucose level of > 140 mg/dl was much higher among women who had undergone a hysterectomy (12.2%) compared to non-hysterectomy women (5.7%). The pattern holds true among relevant background characteristics such as age, place of residence, education, caste, religion, wealth, marital status, body mass index (BMI), anaemia and consumption of tobacco. The adjusted odds after controlling for significant background factors, women who underwent hysterectomy experienced 15% higher odds of blood glucose level of > 140 mg/dl compared those who did not. CONCLUSIONS: The results indicated increased blood glucose level among women post hysterectomy. Hence, the government of India should consider developing evidence-based policies and programming to provide effective targeted interventions for the better reproductive health of women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".