A Review of the Mechanisms, Diagnosis and Preventative Treatment of Osteoporotic Fragility Fractures in Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
The primary association of both type 2 diabetes mellitus (T2DM) and fragility fractures with age has become cause for concern in the developed world, with T2DM now considered an independent risk factor for an increased risk of fragility fracture. The increased susceptibility to fragility fracture associated with T2DM has wide ranging and increasing socioeconomic, morbidity and mortality effects. As the incidence of T2DM increases, understanding the mechanisms behind why T2DM is a causative risk factor to decreased bone health is an important step. These may be split into two broad categories: those that involve an increased risk of falling, and those mechanisms that make fragility fracture after falling more likely due to detrimental changes to bone strength. The latter is not definitively understood making diagnosis in T2DM populations difficult. Current diagnostic methods do not sufficiently account for the unique endocrinological effects of T2DM on bone. New markers for identifying fragility fracture risk in patients with T2DM are required to overcome the paradoxical increase in bone mineral density (BMD) in these populations, and the shortcomings of predictive algorithms and dual energy X-ray absorptiometry (DXA) in identifying fracture risk in T2DM populations. Earlier identification of patients with T2DM who are at risk of fragility fracture is important, as these patients are not as responsive to current preventative medical interventions as those without T2DM, although there are also adoptive lifestyle changes that can help. J Endocrinol Metab. 2015;5(1-2):157-162 doi: http://dx.doi.org/10.14740/jem253w
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".