A parametric analysis of embedded tissue marker properties and their effect on the accuracy of displacement measurements
Bibliographic record
Abstract
Datasets obtained from cadaveric experimentation are broadly used in the validation of finite element models of head injury. Due to the complexity of such measurements in soft tissues, experimentalists have relied on tissue-embedded radiographic or sonomicrometry tracking markers to resolve the motion of the tissue due to impulsive loads. Dynamic coupling of markers with the surrounding tissue has been a previous concern, yet a thorough sensitivity investigation of marker influences on tissue deformation has not been broadly discussed. Technological improvements to measurement precision have bolstered confidence in acquired data, however precision is often conflated with accuracy; the inclusion of markers in the tissue may alter its natural response, resulting in a loss of accuracy associated with an altered displacement field. To gain an understanding of how marker properties may influence the measured response to impact, we prepared a set of nine marker designs using a Taguchi L9 array to investigate marker design choice sensitivity. Each of these designs was cast into a block of tissue simulant an subjected to repeated drop tests. Vertical displacement was measured and compared to the response of the neat material, which contained massless tracking markers. The best performing markers had both a medium density and medium stiffness. The results provide some design guidelines that indicate the importance of maintaining marker:matrix density ratio below 1.75 and marker stiffness below 1.0~MPa to minimize marker interference in tissue deformation. Overall, embedded marker properties must be considered when measuring the dynamic response of tissue.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".