Epidemiology and awareness of osteoporosis: a viewpoint from the Middle East and North Africa
Bibliographic record
Abstract
Background: Osteoporosis (OP) is defined by low bone mass and microstructural deterioration. It is an escalating public health problem due to increase life expectancy and the resulting bone fractures represent a significant burden for both the individual and the society in terms of morbidity, mortality and cost. Osteoporosis, a multifactorial disease, results from the interaction between genetic and environmental risk factors. Currently, the data available regarding OP epidemiology and predisposing risk factors differ greatly between regions and within population ethnicities. Proper estimation of the epidemiology of OP and its health related outcomes can help identity those at risk and permit prophylactic treatment before its occurrence. The main barrier towards disease prevention strategies is the impaired awareness of the disease and its risk. Enhanced understanding of the OP disease may influence personal behaviors and reduce its prevalence. Objectives: This review was undertaken to wrap-up and throw-light on the published literatures related to the epidemiology of osteoporosis in the Middle East and North Africa (MENA) region, and expose the extent of awareness in the corresponding populations. Describing and discussing key points on the current state of knowledge on these hot issues are well thought-out. Conclusion: Osteoporosis prevalence is variable among MENA populations. Limited reports regarding the established prevalence of osteoporotic fractures among those populations and therefore, lack of guidelines for prevention and management were noticed. In order to improve bone health, preventive measures against OP should be considered. Increase OP awareness and preventive practices in the societies as part of the prophylactic strategy need to be initiated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".