Bibliographic record
Abstract
PURPOSE OF REVIEW: Developments in imaging following traumatic brain injury are outlined. Numerous techniques have evolved over the past several years giving us more information about the injury and prognosis for recovery. Some of these techniques are in clinical use while others are used primarily in research but have the potential to become clinically useful. RECENT FINDINGS: Computed tomography (CT) scanning is the primary imaging technique for acute brain injury, giving rapid information and being part of a general trauma work up in the emergency situation. It has supplanted plain films in the immediate management of brain injury. Following stabilization, MRI is the method of choice for evaluating the full extent of brain injury. Information on diffuse axonal injury is obtained by several MRI sequences. Diffusion tensor imaging is able to show long tract damage and relates to prognosis. There are several techniques which are best suited to research in brain injury, including single photon emission CT, PET and xenon CT. SUMMARY: CT and MRI are now the imaging techniques for acute and subacute brain injury, respectively. Diffusion tensor imaging is being developed to provide more information on structural damage in brain injury. There are several research techniques available for brain injury, particularly relating to cerebral blood flow and metabolism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".