In Vitro Nonlinear Viscoelastic Characterization of the Porcine Spinal Cord
Bibliographic record
Abstract
Approximately 12,400 new cases of spinal cord injuries (SCI) are reported in the United States every year. It has been estimated that the annual financial burden of SCI in the United States is approximately $7.736 billion. The mechanisms of mechanical damage to the spinal cord can be broadly classified into distraction, dislocation or contusion. Distraction injuries are predominantly caused by rapid acceleration-deceleration of the cervical spine. Vertebral burst fractures commonly result in contusion of the spinal cord and relative dislocation of adjacent vertebrae can inter-segmentally shear the spinal cord resulting in injury. Multiple studies have examined the quasi-static mechanical properties of the spinal cord [1–3]. However, considering that most spinal cord injuries occur during dynamic events with relatively high strain rates (ex: 10/s), alarmingly few studies have investigated the time-dependent mechanical characteristics of the spinal cord.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".