Analysis of Radiographic Relationship between Distal Radius, Ulna, and Lunate
Bibliographic record
Abstract
Background Wrist anatomy variability is associated with differing susceptibility to pathology. For example, a flat radial inclination is associated with Kienbock's disease. Lunate facet inclination (LFI) also exhibits variability. Its relationship with other wrist features is poorly documented. Purposes We tested the hypothesis that high LFI is associated with increased uncovering of the lunate, negative ulnar variance (UV), and type 2 lunates to balance forces across wrists. Methods In total, 50 bilateral and 100 unilateral wrist posteroanterior radiographs were reviewed. Lunate type, lunate uncovering index (LUI), lunate tilting angle (LTA), UV, and sigmoid notch angle (SNA) were measured, and correlation with LFI was assessed on 150 right wrist radiographs followed by an assessment of differences based on lunate morphology. Symmetry of 50 bilateral wrists was assessed. Results There was no correlation of LFI with lunate morphology, LUI, and LTA. There was a low correlation of LFI with SNA and UV. There was an inverse relationship between UV and SNA. Wrists with type 2 lunates had more oblique sigmoid notches and higher LTA in comparison to wrists with type 1 lunates. Side-to-side comparison revealed strong correlation except for LUI, which exhibited moderate correlation. Conclusions There is no correlation between LFI, LUI, and lunate morphology. Type 2 lunates are associated with higher LTA and more oblique SNA. Wrists were symmetrical. Clinical Relevance Factors other than lunate morphology are essential in balancing forces across wrists. A better understanding of soft tissue and other factors will improve the understanding of wrist biomechanics and pathology. Contralateral wrist radiographs can guide reconstructive surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.003 | 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".