Lumbar Spine Densitometry in People with Spinal Cord Injury: Investigation of Potential Sources of Errors
Bibliographic record
Abstract
PURPOSE: People with spinal cord injury (SCI) experience a considerable loss of bone after the injury. Lumbar spine (LS) bone mineral density (BMD) has been reported to be within the normal range, or even higher when assessed with DXA, in people with SCI; hence, it has been hypothesized that sources of error may spuriously increase LS BMD. The aim of this study was to describe the frequency of potential sources of error that may alter LS BMD measurement in a cohort of individuals with chronic SCI at baseline and over a 2-year period. METHODS: We analyzed baseline and 2-year follow up DXA scans (Hologic Discovery QDR 4500, Hologic Inc., MA, USA) previously performed from a cohort of males and females with chronic SCI. Two physicians independently reviewed each scan, commented on whether the scan was appropriate for BMD analysis, should be re-analyzed, or be removed from the dataset, and reported on the presence of potential sources of error in LS BMD measurement. RESULTS: We reviewed 115 lumbar spine DXA scans from 58 participants, and 107 (93.0 %) scans from 52 participants presented at least one potential source of error. At baseline, the average number of potential sources of error per scan was 5.5 ± 1.7 and 5.7 ± 1.5 according to rater 1 and rater 2, respectively. Follow-up scans presented an average of 5.6 ± 1.6 and 5.7 ± 1.4 potential sources of error according to rater 1 and rater 2, respectively. Facet sclerosis, osteophytes and difficulty in detecting bone edges were the most prevalent sources of error. CONCLUSION: The high frequency of potential sources of error is consistent with current recommendations against the use of LS BMD for fracture risk assessment in people with SCI.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".