Regional and experiential differences in surgeon preference for the treatment of cervical facet injuries: a case study survey with the AO Spine Cervical Classification Validation Group
Bibliographic record
Abstract
PURPOSE: The management of cervical facet dislocation injuries remains controversial. The main purpose of this investigation was to identify whether a surgeon's geographic location or years in practice influences their preferred management of traumatic cervical facet dislocation injuries. METHODS: A survey was sent to 272 AO Spine members across all geographic regions and with a variety of practice experience. The survey included clinical case scenarios of cervical facet dislocation injuries and asked responders to select preferences among various diagnostic and management options. RESULTS: A total of 189 complete responses were received. Over 50% of responding surgeons in each region elected to initiate management of cervical facet dislocation injuries with an MRI, with 6 case exceptions. Overall, there was considerable agreement between American and European responders regarding management of these injuries, with only 3 cases exhibiting a significant difference. Additionally, results also exhibited considerable management agreement between those with ≤ 10 and > 10 years of practice experience, with only 2 case exceptions noted. CONCLUSION: More than half of responders, regardless of geographical location or practice experience, identified MRI as a screening imaging modality when managing cervical facet dislocation injuries, regardless of the status of the spinal cord and prior to any additional intervention. Additionally, a majority of surgeons would elect an anterior approach for the surgical management of these injuries. The study found overall agreement in management preferences of cervical facet dislocation injuries around the globe.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".