The Epidemiology of Scapholunate Advanced Collapse
Bibliographic record
Abstract
Background: Scapholunate advanced collapse (SLAC) is the most common pattern of wrist arthritis. Sparse data exist regarding the SLAC wrist pattern of arthritis. This study aimed to document the epidemiology of advanced SLAC in terms of patients’ sociodemographics and possible association with trauma. Methods: Sixty-one patients with severe SLAC wrist were included. Baseline sociodemographic characteristics were reviewed. To evaluate the relationship to injury, this group of cases was compared with a control group of 61 patients with first carpometacarpal osteoarthritis (CMC OA). The following data were collected for both groups: age, gender, history of traumatic injury, history of manual labor, duration of symptoms, and dominant hand involvement. Pearson chi-square tests for categorical variables and independent samples t test for continuous variables were performed to determine differences between groups. Results: Patients with SLAC wrist were more likely to be male (80.3% vs 31.1%; p<0.001), have a history of a traumatic injury (69.5% vs 25.9%, P < .001), have longer symptom duration (10.3 ± 13.3 vs 3.5 ± 2.5 years, P = .001), be involved in a manual labor job (49.0% vs 20.0%, P = .002), and be younger (53.1 ± 10.4 vs 58.3 ± 9.8; P = .006) compared with patients with CMC OA. There was no difference in dominant hand involvement (49.2% vs 53.3%; P = .571) between the groups. Conclusions: This study identified the characteristics of patients with advanced SLAC wrist. Compared with a control cohort of CMC OA, patients with SLAC wrist were more likely to be male, have a history of a traumatic injury, and be younger.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".