Risk Management of Osteoporosis in Postmenopausal Women; A Study of Women In a Teaching Hospital
Bibliographic record
Abstract
BACKGROUND: Postmenopausal females are susceptible to osteoporosis due to clinical manifestations. It not only causes morbidity; but, is considered to strikingly decline quality of life among patients. Among different developing regions, the prevalence rate of osteoporosis among postmenopausal women is alarming in the face of poor management and awareness about its risk factors. AIM: The aim of this study was to investigate the incidence of osteoporosis and its known risk factors among postmenopausal women appearing for bone mineral density in Karachi. METHODOLOGY: This descriptive cross-sectional study was undertaken from the period of “March 2006 to March 2007” in “Aga Khan University hospital”, Karachi. A total of 245 females, who came to the radiology department at Aga Khan Hospital for DXA scan, were recruited. All the relevant data was collected through questionnaires. Data analysis was undertaken by using SPSS version 11.5 to generate frequencies and proportion percentages. RESULTS: The study demonstrated that 99 females (40%) amongst all subjects were osteopenic, 114 females (47%) were osteoporotic; whereas, 32 females (13%) were normal. A decline was observed in bone mineral density with advancing age and duration of menopause. The distribution of osteoporosis was observed to be common in women, who had more children, low BMI, history of prior fractures, history of premature menopause, and were avoiding exercise. CONCLUSION: This study confirmed a high frequency of osteoporosis and osteopenia in postmenopausal women. Therefore, early screening is required to detect the decrease in bone mineral density among postmenopausal females to prevent fragility fracture. There is an imperative requirement for vast public awareness in this regard.
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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 | 0.001 | 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".