Development of a motion‐controlled in vitro elbow testing system
Bibliographic record
Abstract
Joint simulators can be used to study motion pathways of a human joint, to investigate changes in joint stability following injury, and to formulate improved reconstructive and rehabilitative procedures. Our objectives were: to develop a laboratory-based, motion-controlled elbow testing apparatus capable of simulating tendon (muscle) loading and displacement in a cadaveric specimen; to describe its performance while testing stable and unstable elbows; and to compare its operation to that of a previously designed load-controlled device. Velocity control of a pneumatic actuator was achieved using a custom-written, closed-loop feedback controller. This actuator was incorporated into an elbow testing system that used additional pneumatic actuators and a combination of motion- and load-control to achieve desired motions. Simulations achieved with this apparatus demonstrated small magnitudes of error in actuator position and highly repeatable flexion pathways with the specimens positioned in vertical, varus, and valgus orientations. The repeatability in motion pathways generated in both a stable and unstable elbow model was equivalent to or better than for similar tests performed using the load-controlled system, and the velocity of the resulting elbow motion was more reproducible.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".