Assessment of Osteoporosis Using Standard Radiographs of the Wrist
Bibliographic record
Abstract
BACKGROUND: This study evaluated the ability of the orthopaedic surgeon to radiographically assess bone density in the wrist with sufficient accuracy to determine which patients require treatment for osteoporosis. METHODS: Thirty-eight patients with unilateral distal radius fractures, 30 of whom were female, were included in this study. The mean age was 55 years (range 45 to 82). Standard radiographs of the fractured and normal wrists were taken. Dual energy x-ray absorptiometry was performed on the normal distal radius of all patients within 1 week of their injury. The radiographs were viewed in blinded randomized fashion on two separate occasions by three orthopaedic surgeons and once by a fourth. The participants were required to determine the presence of osteoporosis. Visual analog scales (VAS) were used to evaluate (1) porosity, (2) cortical thickness, (3) trabecular thickness, and (4) the number of trabeculae in the ultradistal radius. RESULTS: Intraobserver agreement assessing osteoporosis averaged 81% (kappa of 0.5393). VAS assessment was unreliable for all four parameters. Radiographic determination of osteoporosis had a specificity of 61% and a sensitivity of 61% using x-rays of the uninjured wrist. CONCLUSION: We conclude that orthopaedic surgeons cannot predict with sufficient accuracy using plain x-rays whether a patient is significantly osteoporotic.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".