Postmenapozal Osteoporotik Kadınlarda Fibromiyalji Sıklığı ve İlişkili Risk Faktörlerinin Belirlenmesi (Ön Çalışma)
Bibliographic record
Abstract
Objective: The purpose of the study was to determine the prevalence of fibromyalgia in postmenopausal women with osteoporosis and to determine the associated factors with fibromyalgia.Materials and Methods: Hundred thirty-seven postmenopausal women with osteoporosis admitted to our outpatient clinic were included in the study.A questionnaire that was including patients'age, marital status, education level, occupation, height, weight was completed.Risk factors of osteoporosis were recorded.major and minor risk factors were determined according to Canadian diagnosis and treatment guideline.DXA was used to determine bone mineral density of the lumbar spine and femoral neck.Thoracal and lumbar compression fractures were evaulated with thoracal and lumbar radiography.The diagnosis of fibromyalgia was according to the 1990 American College of Rheumatology (ACR) criteria.Results: Twenty-six (19%) of 137 postmenopausal women with osteoporosis had fibromyalgia.Ninety-four (68.6%) of all patients were married and 43 (31.4%) of all patients were not married, 63 (46%) of all patients were literate, 74 (54%) of all patients were illiterate.The average age of patients was 73.56±6.17.According to the results of logistic regression analysis, advanced age, to be married, the number of major risk factors and the decrease of lomber and femur bone mineral density were found to be risk factors for fibromyalgia.Educational level, BMI and the number of minor risk factors were not found to be a risk factors for fibromyalgia.Conclusion: It is importatnt to be careful for fibromyalgia not only in premenopousal women but also in postmenopausal osteoporotic women.(Turkish Journal of Osteoporosis 2014;20: 1-5)
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".