A strain-based investigation of the accuracy of embedded markers used in tracking cadaveric brain motion
Bibliographic record
Abstract
Measurements of intracranial brain displacement in cadaveric specimens have been instrumental to the validation finite element (FE) models of brain injury. These data collections have used radiographic and sonomicrometric techniques, requiring the use of tissue-embedded tracking markers; however, marker accuracy has never been adequately characterized. Marker tracking precision has been previously conflated with measurement accuracy, not accounting for changes in the natural responseof surrounding tissues due to marker presence. Non-negligible inertia, high stiffness, and the aspect ratio of markers all contribute to this interference. This work investigated the dynamic coupling between published marker designs (NDTs, Sonomicrometry Crystals, and Tin) and a new elastomeric marker, and a block of tissue simulant subjected to a drop impact. The measured strains were compared to the baseline response of the simulant containing massless markers. The results found notable evidence of interference in simulant strain amplitudes as well as considerable directional bias in the response of some markers. The elastomeric marker was found to have minimal interference in the deformation field. FutureFE model validation will need to account for the considerable interference and directional biases to the natural response of brain tissue in existing cadaveric datasets to maintain confidence in strain predictions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".