[How Bone Densitometry contributes to the diagnosis of Osteoporosis at the Gerontology and Geriatrics Center of Ouakam, Dakar City: 102 observation].
Bibliographic record
Abstract
INTRODUCTION: This study aims at assessing the morbidity rate and risk factors of osteoporosis. METHODS: This is a cross-sectional study conducted at the Gerontology and Geriatrics Center of Ouakam from 2010 to 2012 with patients who had benefited from a bone densitometry examination. The study variables were: sex, current age, menopausal age, past individual fractures, past family fractures, prolonged corticoid intake, prolonged immobilization, tobacco addiction, and alcoholism. Epi-info version 6 Software was used to process and analyze the data. RESULTS: The study included 102 patients the majority of which were women (75%), with an average age of 74 years; among these people, 31% had individual past fractures and 10% had family fractures; 8% of the patients had used a prolonged corticoid-based therapy and 2% were tobacco-addicts. The average age of menopause was 48 years. The prevalence rate for osteoporosis was 44%, based on Rachis T-score, 20%, based on Femur T-score and 45%, based on Rachis and Femur T-score. It has been noted that being a woman and past individual fractures rated among the major risk factors of osteoporosis. CONCLUSION: These results speak in favor of health authorities setting-up a program to fight osteoporosis centered mainly on primary prevention and screening.
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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 | 0.002 |
| 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) | 0.004 | 0.001 |
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".