Rapid magnetic resonance imaging–guided reduction of craniovertebral junction deformities
Bibliographic record
Abstract
The authors demonstrate the utility of an MR imaging-compatible traction board for the rapid reduction of craniovertebral junction (CVJ) deformities. To choose the appropriate surgical management, patients with compressive CVJ deformities often undergo a trial of traction. Conventional traction trials require the treating surgeon to infer from plain radiographs the manner in which traction forces affect neural and ligamentous structures at the CVJ. To avoid overdistraction injury, low increments of weight are added in a gradual fashion, a process that typically requires 48-72 hours. The authors outline the use of an MR imaging-compatible traction board to determine reducibility safely and rapidly in 4 patients with compressive CVJ deformities. Four patients with advanced CVJ deformities underwent a trial of MR imaging-guided traction performed using an MR imaging-compatible spine board. Serial sagittal images were acquired at baseline and following each sequential addition of force. All patients tolerated traction without neurological worsening. The neural elements were seen to be adequately decompressed in all cases during a single MR imaging session. Patients subsequently underwent craniocervical stabilization and fusion. Postoperative imaging showed maintenance of the reduction without neural compression. An MR imaging-guided trial of traction can facilitate the rapid and safe determination of the reducibility of compressive lesions in patients with advanced CVJ deformities. Rapidly acquired sagittal MR images permit the surgeon to evaluate the effects of traction on the soft tissues at the CVJ, thereby expediting the traction trial and avoiding the risks of immobility in this often-fragile patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| 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".