Current Practice of Acute Spinal Cord Injury Management: A Global Survey of Members from the AO Spine
Bibliographic record
Abstract
Study Design Cross-sectional, international survey. Objectives To examine current international practices as well as knowledge, adoption, and barriers to guideline implementation for acute spinal cord injury (SCI) management. Methods A survey was distributed to members of AO Spine. The questionnaire was structured to obtain demographic data and preferred acute SCI practices surrounding steroid use, hemodynamic management, and timing of surgical decompression. Results 593 members completed the survey including orthopaedic surgeons (54.3%), neurosurgeons (35.6%), and traumatologists (8.4%). Most (61.2%) respondents were from low and middle-income countries (LMICs). 53.6% of physicians used steroids for the treatment of acute SCIs. Respondents from LMICs were more likely to administer steroids than HICs (178 vs. 78; P < .001). 331 respondents (81.5%) answered that patients would receive mean arterial pressure (MAP) targeted treatment. In LMICs, SCI patients were less likely to be provided with MAP-targeted treatment (76.9%) as compared to HICs (89%; P < .05). The majority of respondents (87.8%) reported that patients would benefit from early decompression. Despite overwhelming evidence and surgeons’ responses that would offer early surgery, 62.4% of respondents stated they encounter logistical barriers in their institutions. This was particularly evident in LMICs, where 57.9% of respondents indicated that early intervention was unlikely to be accomplished, while only 21.1% of respondents from HICs stated the same ( P < .001). Conclusion This survey highlights challenges in the implementation of standardized global practices in the management of acute SCI. Future research efforts will need to refine SCI guidelines and address barriers to guideline implementation.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".