Quantifying Intraparenchymal Hemorrhage after Traumatic Spinal Cord Injury: A Review of Methodology
Bibliographic record
Abstract
Intraparenchymal hemorrhage (IPH) after a traumatic injury has been associated with poor neurological outcomes. Although IPH may result from the initial mechanical trauma, the blood and its breakdown products have potentially deleterious effects. Further, the degree of IPH has been correlated with injury severity and the extent of subsequent recovery. Therefore, accurate evaluation and quantification of IPH following traumatic spinal cord injury (SCI) is important to define treatments' effects on IPH progression and secondary neuronal injury. Imaging modalities, such as magnetic resonance imaging (MRI) and ultrasound (US), have been explored by researchers for the detection and quantification of IPH following SCI. Both quantitative and semiquantitative MRI and US measurements have been applied to objectively assess IPH following SCI, but the optimal methods for doing so are not well established. Studies in animal SCI models (rodent and porcine) have explored US and histological techniques in evaluating SCI and have demonstrated the potential to detect and quantify IPH. Newer techniques using machine learning algorithms (such as convolutional neural networks [CNN]) have also been studied to calculate IPH volume and have yielded promising results. Despite long-standing recognition of the potential pathological significance of IPH within the spinal cord, quantifying IPH with MRI or US is a relatively new area of research. Further studies are warranted to investigate their potential use. Here, we review the different and emerging quantitative MRI, US, and histological approaches used to detect and quantify IPH following SCI.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".