Forearm Rotation at the Time of Elbow Ulnar Collateral Ligament Reconstruction Graft Tensioning Does Not Affect Postoperative Medial Elbow Joint Gapping
Bibliographic record
Abstract
Background: The role of forearm rotation at the time of ulnar collateral ligament (UCL) reconstruction (UCLR) graft tensioning is poorly understood. Purpose: To compare postoperative medial elbow joint gapping in cadaveric elbows after UCLR graft tensioning with the forearm in supination versus pronation. Study Design: Descriptive laboratory study. Methods: A total of 18 full-arm human specimens were stripped of soft tissue except elbow ligamentous and capsular structures. Elbows with an intact, native ligament were tested for medial elbow gapping during valgus stress at 30°, 60°, and 90° with the forearm in neutral, maximal supination, and maximal pronation. Joint gapping was determined with a 3-dimensional motion capture system and calibrated digitized points on the ulna and humerus. The UCL was transected, and reconstruction was performed using a standard docking technique. Elbows were randomized to full supination or pronation at the time of final graft tensioning, and medial elbow joint gapping was again measured for the same positions and same technique. Analysis of variance test was used to compare differences in native and postoperative medial elbow joint gapping ( P < .05). Results: The position of forearm rotation did not affect the amount of medial elbow joint gapping during valgus stress at all tested elbow flexion angles with an intact, native UCL. The position of forearm rotation during UCL graft tensioning also did not affect postoperative medial elbow joint gapping during valgus stress at all tested elbow flexion angles. Conclusion: Forearm rotation with an intact UCL and at the time of UCL graft tensioning did not affect the amount of medial elbow joint gapping during valgus torque.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".