Development of a Testing Methodology to Quantify Bone Load Transfer Patterns for Multiple Stemmed Implants in a Single Bone With an Application in the Distal Ulna
Bibliographic record
Abstract
Optimal parameters for many orthopaedic implants, such as stem length and material, are unknown. Geometry and mechanical properties of bone can vary greatly amongst cadaveric specimens, requiring a large number of specimens to test design variations. This study aimed to develop an experimental methodology to measure bone strains as a function of multiple implant stem designs in a single specimen, and evaluate its efficacy in the distal ulna. Eight fresh-frozen cadaveric ulnae were each instrumented with 12 uniaxial strain gauges on the medial and lateral surfaces of the bone. The proximal portion of each ulna was cemented in a custom-designed jig that allowed a medially directed force to be applied to the distal articular surface. An implant with a finely threaded stem was cemented into the canal by an experienced upper extremity orthopaedic surgeon. Six loads (5-30 N) were applied sequentially to the lateral surface of the prosthetic head using a materials testing machine. Testing was repeated after breaking the stem-cement bond, and after removing and reinserting the stem several times into the threaded cement mantle. Near the end of the testing period, the initial stem was reinserted and data were collected to determine if there was any change in bone properties or testing setup over time. Finally, a smooth stem was inserted for comparison to the threaded stem. Strain varied linearly with load (R(2)> or =0.99) for all testing scenarios. Bending strains were not affected by breaking the stem-cement bond (P=0.7), testing durations up to 18 h (P=0.7), nor the presence of threads when compared to a smooth stem (P>0.4). Furthermore, for all gauges, there was no interaction between the effect of the threads and level of applied load (P>0.1). This methodology should prove to be useful to compare stem designs of varying lengths and materials in the same bone, allowing for a direct comparison between implant designs for the ulna and other bones subjected primarily to bending loads. Furthermore, it will minimize the need for large numbers of specimens to test multiple implant designs. The ultimate goal of using this protocol is to optimize implant stem properties, such as length and material, with respect to load transfer.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".