Retracted: Bone Mineral Density Predicts Fractures in Chronic Kidney Disease
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Fractures are common in chronic kidney disease (CKD). The optimal methods by which to assess fracture risk are unknown, in part, due to a lack of prospective studies. We determined if bone mineral density (BMD) by dual-energy X-ray absorptiometry (DXA), and/or high-resolution peripheral quantitative computed tomography (HRpQCT) could predict fractures in men and women ≥18 years old with stages 3 to 5 CKD. BMD was measured by DXA (at the total hip, lumbar spine, ultradistal, and 1/3 radius) and by HRpQCT (at the radius), and subjects were followed for 2 years for incident morphometric spine fractures and low-trauma clinical fractures. The mean age of the subjects was 62 years with equal numbers having stages 3, 4, and 5 CKD. Over 2 years there were 51 fractures in 35 subjects. BMD by DXA at baseline was significantly lower at all sites among those with incident fractures versus those without. For example, the mean BMD at the total hip in those with incident fractures was 0.77 g/cm2 (95% confidence interval [CI], 0.73 to 0.80) and in those without fracture was 0.95 g/cm2 (95% CI, 0.92 to 0.98). Almost all baseline HRpQCT measures were lower in those with incident fracture versus those without. For example, volumetric BMD in those with incident fractures was 232 mg HA/cm3 (95% CI, 213 to 251) and in those without fracture was 317.6 mg HA/cm3 (95% CI, 306 to 329.1). Bone loss occurred in all subjects, but was significantly greater among those with incident fractures. Our data demonstrate that low BMD (by DXA and HRpQCT) and a greater annualized percent decrease in BMD are risk factors for subsequent fracture in men and women with predialysis CKD.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".