Documenting Cervical Spine Injuries Following Negative MRI Findings: Clinical and Medico-Legal Overview of Dynamic Imaging
Bibliographic record
Abstract
Because of the lack of uniformity in describing injuries to the cervical spine following trauma, such as motor vehicle accidents, the Government of Quebec convened a multidisciplinary task force in the 1990s to attempt to standardize the terminology for the classification, management, and prognosis for such injuries. The term "whiplash-associated disorders" (WAD) was adopted and has become the universally recognized umbrella term for the myriad of symptoms caused by severe acceleration and deceleration forces applied to the head, craniocervical junction, and cervical spine following trauma. Obtaining an accurate diagnosis and prognosis for individuals with WAD is challenging, especially when magnetic resonance imaging and computed tomography findings are negative. This becomes more critical when litigation is involved. However, the often overlooked and underutilized imaging modality of video fluoroscopy/dynamic imaging is essential for the diagnosis of ligament instability, which can lead to hypolordosis of the cervical spine and has a long list of potential clinical consequences. The review highlights the usefulness of the cervical curve as a clinical indicator of the severity of vertebral column injuries.
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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 | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".