The spectrum of ulnar collateral ligament injuries as viewed on magnetic resonance imaging of the metacarpophalangeal joint of the thumb.
Bibliographic record
Abstract
OBJECTIVE: To elucidate the spectrum of ulnar collateral ligament (UCL) injuries detectable by magnetic resonance imaging (MRI). METHODS: Twenty-one patients (12 male and 9 female, aged 14-62 years) with acute hyperabduction injuries of the first metacarpophalangeal joint underwent MRI for clinically suspected UCL injuries. All scans were performed in either a large-bore, 1.5-T imager or an experimental small-bore, 1.9-T imager. MRI findings and clinical evaluations of all patients and surgical reports of those who underwent surgery (n = 10) were reviewed and correlated retrospectively. RESULTS: A total of 6 patients demonstrated injuries that did not fall into previously described categories of UCL injuries and therefore illustrated the existence of a subclass of UCL injuries. We divided the MRI findings into 5 categories: Stener's lesions (n = 6), moderately displaced (> or = 3 mm) complete tears (n = 5), minimally displaced (< 3 mm) complete tears (n = 4), nondisplaced complete tears (n = 3) and partial tears (n = 3). None of the MRI scans demonstrated a normal UCL. Although sensitivity and specificity were not calculated, only 2 cases demonstrated discordance between the MRI results and surgical findings or clinical outcomes. CONCLUSION: There is a spectrum of UCL injuries that have not previously been described.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".