Diversity and Commonalities in the Care of Spine Trauma Internationally
Bibliographic record
Abstract
STUDY DESIGN: Questionnaires administered to practicing orthopedic and neurosurgical spine surgeons from North America, Europe, Asia, Australia, and New Zealand. OBJECTIVE: To determine diversity and commonalities in the treatment of spine trauma internationally. SUMMARY OF BACKGROUND DATA: Previous studies have had suggested that there is significant variability in the treatment of common spinal trauma conditions. METHODS: Questionnaires based on 10 commonly encountered spine trauma cases were administered to 77 experienced spinal surgeons across 4 continents. The questions for each case scenario were similar, but were tailored to be appropriate for the case being presented. Questions focused on the discipline of the specialist who would treat the injury, the treatment itself, and how long it would take for the patient to return home or to work. Questions pertaining to investigations, funding, estimated in-hospital length of stay, prehospital care, and mortality were also included. Each question was followed by 4 to 8 options from which the surgeon could chose a response. Data were analyzed using, SAS v9.2, a software analytical package, to determine the level of agreement between respondents on different items. RESULTS: Of the 77 surgeons completing the questionnaire, 66% were fellowship trained spine surgeons, 20% orthopedic surgeons, and 14% neurosurgeons. In all regions, the majority of spinal trauma care is provided in a regional trauma center with dedicated spine surgeons. In all but 1 case significant differences were found in treatment with European and Asian surgeons have a higher operative rate. A combined anterior/posterior surgical approach was more commonly used in Europe and Asia. North American patients are expected to have a shorter hospital stay and return to work earlier. Significant differences exist between Asia and the other regions with regard to prehospital care. CONCLUSION: These findings suggest that despite the subspecialty focus in the care of spinal trauma, there continues to exist a lack of consensus among treating surgeons as to the optimum management and appropriateness of multiple aspects of specific operative and nonoperative interventions and indeed the postinjury management.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".