Subaxial spine arthrodesis in patients with spine fractures and facet joint dislocations: Is magnetic resonance imaging required to determine the optimal surgical approach?
Bibliographic record
Abstract
BACKGROUND: The medical literature suggests that facet dislocations (FDs) must be managed surgically, even in the absence of spinal cord injury. In fact, there is no standard guideline for managing FD cases and whether magnetic resonance imaging (MRI) should be utilized for optimizing treatment planning. METHODS: Fifteen cases of FD were evaluated twice by nine spine surgeons. The first assessment included computed tomography (CT) images only. Secondarily, original CT studies were supplemented with MRI. In each case, the participating surgeon had to acknowledge whether and what surgical treatment they would offer. Data for the two responses from all nine surgeons were then compared. RESULTS: Based on CT images alone, there was no consensus regarding treatment choices in 13 cases, and a trend toward consensus in just two instances (κ = 0.01). When MRI scans were added to CT studies, among the 15 cases evaluated, 10 cases demonstrated a trend toward consensus, and in 1 case consensus was achieved. The Kappa interpersonal agreement based on MRI was 0.13. The analysis of the answers by each contributor in each case demonstrated that in 58.51% of cases the surgical treatment options were changed when analyzed by CT + MRI, in comparison to the options indicated based on CT alone. CONCLUSION: It appears that obtaining an MRI in addition to a CT before spine surgery for FD is essential mandatory, as it changed the treatment option in nearly 60% of cases.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".