Risk factors for osteoporosis in elderly patients with type 2 diabetes: A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Osteoporosis is a prevalent chronic result of diabetes. Osteoporosis susceptibility is raised by unstable blood glucose levels, oxidative stress, hormonal abnormalities, and other factors. Currently, there is no systematic review addressing the risk factors of osteoporosis in diabetes. This study intends to systematically assess the current risk factors related to diabetic osteoporosis (DOP) and provide suggestions for the improvement of therapy approaches. METHODS AND ANALYSIS: We will search five English literature databases (PubMed, Embase, Web of Science, CINAHL, and Cochrane Library) and three Chinese databases (CNKI, WanFang, and SinoMed) from the starting point until December 31, 2024. We will perform a systematic examination and meta-analysis of cohort and case-control studies to identify all population-based risk factors for diabetic osteoporosis. Two researchers will independently assess the publication, extract data, and evaluate the quality and potential biases present in the study. We will utilize RevMan V.5.4 software and STATA 16.0 for data analysis. The included studies will be assessed using the Newcastle Ottawa Quality Assessment Instrument (NOS). If the heterogeneity of the included studies is higher than 50%, we will perform subgroup and sensitivity analysis to identify probable sources of heterogeneity. The assessment of publication bias will be conducted using funnel plot. Furthermore, we will employ the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) to assess the quality of evidence for each exposure and outcome. DISCUSSION: This protocol aims to investigate the risk variables associated with DOP. We will summarize the current knowledge about factors influencing osteoporosis in diabetes. We strive to assist physicians with more extensive references for decision-making and facilitate the implementation of effective prevention strategies for DOP. REGISTRATION: This study has been registered in the PROSPERO (CRD42024602637).
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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 | 0.076 | 0.109 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.004 |
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".