Study epidemiology of risk fracture in osteoporosis based on frax score, and osta score, with risk of fall using Ontario score in elderly Indonesia
Bibliographic record
Abstract
Background: Osteoporosis is characterized by a low bone mass of bone tissue. If osteoporosis is not treated properly, it will increase the high risk of fracture. The common causes of fracture on osteoporosis condition due to falls. This study aims to find the correlation between the risk of osteoporosis with fall risk (ONTARIO) based on osteoporosis fracture risk (FRAX) Methods: This study is an analytic study with a cross-sectional method. We collected the sample using random cluster sampling in the six primary health care in Malang on different times service since August–September 2021. Total patient 139, however only 132 patients were included in this study. After collecting data is complete, we analyze using Chi-square tests. Results: The mean age of participants was 63.9 ± 7.14. with the age group was dominated by the range of 60–64. It was found that the result of the FRAX SCORE had a low-risk category for major fracture osteoporosis and risk hip fracture. In contrast, from the OSTA score in this study, more than 68 participants (50.8%) were found medium and high-risk scores. Then, in ONTARIO score of the risk fall assessment, and high score in 57 participants (43.2%). If compared between OSTA and ONTARIO, there was a significant relationship between OSTA score and ONTARIO score ( p < 0.000) with high-risk OSTA have a significant relationship with a high risk of falling and vice versa. Conclusion: In this study, there was a relationship between the risk of high osteoporosis and the risk of falling.
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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 | 0.002 |
| 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) | 0.001 | 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".