Evaluation of Bone Health among Type 2 Diabetes Mellitus Patients
Bibliographic record
Abstract
Background: Type 2 diabetes mellitus (T2DM) and osteoporosis remain one of the major public health problems worldwide with a considerable burden on society. Health belief toward osteoporosis is fundamental to all osteoporosis management programs and is often a pre-requisite for initiating desired behavioral changes. The aim of this study was to assess: the level of the Malay version of the Osteoporosis Health Belief Scale (OHBS-M) among T2DM patients; the relation of socio-demographic characteristics, clinical data with OHBS-M level and the correlation between OHBS-M score and T-score. Methods: An observational, cross-sectional study design was conducted among T2DM patients. Socio-demographic and clinical data were collected using a convenient sampling method. All T2DM patients underwent the bone mineral density measurement using a quantitative ultrasound scan (QUS). Results: The result showed the average age of the participants was 62.67± 9.24 years. The study findings revealed that the average total score of OHBS-M 143.08±24.22 (median 141.50) with 85.60% of T2DM patients had a low level of osteoporosis health belief. Moreover, a significant correlation was found between the QUS T-scores and osteoporosis health beliefs. Conclusions: The study findings revealed that the assessment of T2DM patients’ bone health and health belief toward osteoporosis is crucial to improve an osteoporosis preventive strategy for high-risk populations.
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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 | 0.000 | 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) | 0.002 | 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".