Prevention, Diagnosis, and Management of Intraoperative Spinal Cord Injury in the Setting of Spine Surgery: A Proposed Care Pathway
Bibliographic record
Abstract
STUDY DESIGN: This study is a mixed methods approach. OBJECTIVES: Intraoperative spinal cord injury (ISCI) is a challenging complication in spine surgery. Intra-operative neuromonitoring (IONM) has been developed to detect changes in neural function. We report on the first multidisciplinary, international effort through AO Spine and the Praxis Spinal Cord Institute to develop a comprehensive guideline and care pathway for the prevention, diagnosis, and management of ISCI. METHODS: Three literature reviews were registered on PROSPERO (CRD 42022298841) and performed according to PRISMA guidelines: (1) Definitions, frequency, and risk factors for ISCI, (2) Meta-analysis of the accuracy of IONM for diagnosis of ISCI, (3) Reported management approaches for ISCI and related events. The results were presented in a consensus session to decide the definition of IONM and recommendation of its use in high-risk cases. Based on a literature review of management strategies for ISCI, an intra-operative checklist and overall care pathway was developed by the study team. RESULTS: An operational definition and high-risk patient categories for ISCI were established. The reported incidence of deficits was documented to be higher in intramedullary tumour spine surgery. Multimodality IONM has a high sensitivity and specificity. A guideline recommendation of IONM to be employed for high-risk spine cases was made. The different sections of the intraoperative checklist include surgery, anaesthetic and neurophysiology. The care pathway includes steps (1) initial clinical assessment, (2) pre-operative planning, (3) surgical/anaesthetic planning, (4) intra-operative management, and (5) post-operative management. CONCLUSIONS: This is the first evidence based comprehensive guideline and care pathway for ISCI using the GRADE methodology. This will facilitate a reduction in the incidence of ISCI and improved outcomes from this complication. We welcome the wide implementation and validation of these guidelines and care pathways in prospective, multicentre studies.
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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 | 0.068 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".