Prevalence of Vertebral Fractures in Adults With Type 1 Diabetes: DenSiFy Study (Diabetes Spine Fractures)
Bibliographic record
Abstract
CONTEXT: Vertebral fracture (VF) prevalence up to 24% has been reported among young people with type 1 diabetes (T1D). If this high prevalence is confirmed, individuals with T1D could benefit from preventative VF screening. OBJECTIVE: We compared the prevalence of VFs between adults with T1D and nondiabetic controls. METHODS: This cross-sectional study included 127 adults with T1D, and 65 controls with a similar age, sex, and BMI distribution, from outpatient clinics of 2 tertiary care centers. Vertebral fracture assessment (VFA) by dual-energy x-ray absorptiometry (DXA) was used for prevalent VFs. The modified algorithm-based qualitative (mABQ) method was applied. Bone mineral density (BMD) and trabecular bone score (TBS) were assessed by DXA. Serum bone turnover markers and sclerostin were measured in a subgroup of participants. RESULTS: Participants with T1D (70 women, 57 men) had a mean age of 42.8 ± 14.8 years, median diabetes duration of 25.8 (15.8-34.4) years, mean BMI of 26.6 ± 5.4 kg/m2 and mean HbA1c over the past 3 years of 7.5 ± 0.9%. Controls (35 women, 30 men) had mean age of 42.2 ± 15.9 years and mean BMI of 26.1 ± 5.1 kg/m2. VF prevalence was comparable between groups (2.4% vs 3.1%, P = 0.99). TBS, BMD at the total hip and femoral neck, and bone formation and resorption markers were lower while sclerostin levels were similar in participants with T1D vs controls. CONCLUSION: Our VFA results using the mABQ method do not confirm increased prevalence of VFs in men and women with relatively well-controlled T1D.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".