Effect of simulated muscle activity on distal radioulnar joint loading in vitro
Bibliographic record
Abstract
This study investigated the relationship between simulated forearm muscle loads and the joint reaction force in the distal radioulnar joint using an in vitro model. Seven fresh frozen cadaveric specimens were mounted in an upper extremity joint simulator capable of applying pneumatic loads to various (muscle) tendons while restraining the forearm in the three positions of pronation, supination, and neutral rotation. Loads were applied to model four forearm muscles (biceps, pronator teres, pronator quadratus, and supinator) in 10 N increments ranging from 10 N to 80 N for the biceps and pronator teres and in 10 N increments from 10 N to 50 N for the pronator quadratus and the supinator. Distal ulnar arthroplasty was performed on each specimen with a custom instrumented ulnar head replacement implant that quantified loads (via strain gauge instrumentation). The relationship between increasing muscle load and joint load was found to be positive and quasilinear in most cases. The biceps had the greatest influence on the distal radioulnar joint reaction force with a joint force in the range of 8% to 33% of the applied muscle load. The pronator teres, supinator, and pronator quadratus were less influential with a joint reaction force ranging between 6% to 19%, 4% to 9% and 2% to 10% of the applied muscle load, respectively.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".