The Management of Intraoperative Spinal Cord Injury – A Scoping Review
Bibliographic record
Abstract
STUDY DESIGN: Scoping Review. OBJECTIVE: To review the literature and summarize information on checklists and algorithms for responding to intraoperative neuromonitoring (IONM) alerts and management of intraoperative spinal cord injuries (ISCIs). METHODS: MEDLINE® was searched from inception through January 26, 2022 as were sources of grey literature. We attempted to obtain guidelines and/or consensus statements from the following sources: American Association of Neuromuscular & Electrodiagnostic Medicine (AANEM), American Academy of Neurology (AAN), American Clinical Neurophysiology Society, NASS (North American Spine Society), and other spine surgery organizations. RESULTS: Of 16 studies reporting on management strategies for ISCIs, two were publications of consensus meetings which were conducted according to the Delphi method and eight were retrospective cohort studies. The remaining six studies were narrative reviews that proposed intraoperative checklists and management strategies for IONM alerts. Of note, 56% of included studies focused only on patients undergoing spinal deformity surgery. Intraoperative considerations and measures taken in the event of an ISCI are divided and reported in three categories of CONCLUSION: There is a paucity of literature on comparative effectiveness and harms of management strategies in response to an IONM alert and possible ISCI. There is a pressing need to develop a standardized checklist and care pathway to avoid and minimize the risk of postoperative neurologic sequelae.
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 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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".