Evaluating The Risk Of Osteoporosis Through Bone Mass Density.
Bibliographic record
Abstract
BACKGROUND: Osteoporosis is a bone disorder, characterized by loss of bone mass density. Osteoporosis affects more than 30% of post-menopausal women. Osteoporosis is often associated with restricted body movement, pain and joint deformities. Early identification and early intervention can help in reducing these complications. The primary objective of this study was to estimate the burden of Osteoporosis in Urban setting of Sindh among women of different age groups and to access the effect of different protective measures that can reduce the risk of Osteoporosis. METHODS: In this study, 500 women's of 3 major cities of Sindh were approached by non-probability convenience sampling technique. Women bearing age 20 years or more were included. Women who fall under inclusion criteria were screened for BMD (Bone mineral density) test and were classified as Healthy, Osteopenic and Osteoporotic based on their T-score. The association of different protective measures and risk of osteoporosis was assessed by prevalence relative risk (PRR). RESULTS: The result of this study indicate that the burden of Osteoporosis is very high among the women of Sindh, only 17.4% (84) women were found to have normal BMD score. The life style of majority of women was sedentary. The PRR calculated for Exposure to sunlight, regular exercise, and use of nutritional supplement was 12.5, 5.19 and 2.72 folds respectively. CONCLUSIONS: The results of study reveal that exposure to sunlight, regular physical exercise and use of nutritional supplements found to be effective in reducing the risk of osteoporosis among women of all age group. Health education and promotion toward osteoporosis prevention can significantly contribute in reducing the morbidity of osteoporosis.
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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 | 0.001 | 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 | 0.000 | 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".