An intra‐bone axial load transducer: development and validation in an in‐vitro radius model
Bibliographic record
Abstract
BACKGROUND: Accurate measurement of forces through the proximal radius can assess the effects of some surgical procedures on radioulnar load sharing, but is difficult to achieve given the redundant loading nature of the musculoskeletal system. Previously reported devices have relied on indirect measurements that may alter articular joint location and function. An axial load transducer interposed in the diaphysis of the radius may accurately quantify unknown axial loads of the proximal radius, and maintain articular location. METHODS: An in-vitro radius model was developed by interposing an axial load transducer in the diaphysis of the proximal radius. Static loads of 20, 40, 60, 80, and 100 N were applied with a servo-hydraulic actuator to the native radial head at angles of 10°, 20°, 30°, and 40° in the anterior, posterior, medial and lateral directions. FINDINGS: Linear regression of five repeatability trials showed excellent agreement between the transducer and applied loads (R (2) = 1 for all trials). For off-axis net joint loads, the majority of measured loading errors were within the inter-quartile range for mean loads up to 80 N. Loads below 80 N and outside the inter-quartile range had errors of less than 1 N. CONCLUSIONS: The repeatability and off-axis net joint load results of this study validate the effectiveness of the interposed axial load transducer to accurately quantify proximal radius loads. The surgical technique preserves the native articular location and soft-tissue constructs, like the annular ligament. The modular design allows for testing the effects of length-changing osteotomies in subsequent biomechanical studies.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".