Impact of Body Mass Index on Menstrual Health: A study among Tripura’s Tribal Communities
Bibliographic record
Abstract
The menstrual cycle is one important metric for assessing the quality of life and reproductive health of women. A high or low body mass index (BMI) might put a woman at risk for menstrual abnormalities, which can include painful periods, irregular cycles, and lack of menstruation. This investigation seeks to ascertain the relationship between menstrual cycle issues and body mass index in tribal students of Tripura. This study used an observational analytic study design and a cross-sectional approach. Samples are selected using a purposive sampling technique. The samples were a total of 100 individuals of undergraduate students belongs to ‘Scheduled Tribes’ community within the age range of 19-22 years old. Based on the chi-square statistical test, the results obtained with p- value= 0,000 (p-value *** P<0.001) which indicates menstrual cycle abnormalities and Body Mass Index (BMI) are significantly correlated in students.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".