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 validating 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 tissue motion caused by 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 and subjected to repeated drop tests. Vertical displacement was measured and compared to the response of the neat material, which contained massless tracking markers. Medium density and medium stiffness markers yielded the least deviation from the neat material response. The results provide some design guidelines indicating the importance of maintaining marker matrix density ratio below 1.75 and marker stiffness below 1.0 MPa. These properties may minimize marker interference in tissue deformation. Overall, embedded marker properties must be considered when measuring the dynamic response of tissue.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".