Prevention, identification, and treatment of perioperative spinal cord injury
Bibliographic record
Abstract
OBJECT: In this report, the authors suggest evidence-based approaches to minimize the chance of perioperative spinal cord injury (POSCI) and optimize outcome in the event of a POSCI. METHODS: A systematic review of the basic science and clinical literature is presented. RESULTS: Authors of clinical studies have assessed intraoperative monitoring to minimize the chance of POSCI. Furthermore, preoperative factors and intraoperative issues that place patients at increased risk of POSCI have been identified, including developmental stenosis, ankylosing spondylitis, preexisting myelopathy, and severe deformity with spinal cord compromise. However, no studies have assessed methods to optimize outcomes specifically after POSCIs. There are a number of studies focussed on the pathophysiology of SCI and the minimization of secondary damage. These basic science and clinical studies are reviewed, and treatment options outlined in this article. CONCLUSIONS: There are a number of treatment options, including maintenance of mean arterial blood pressure > 80 mm Hg, starting methylprednisolone treatment preoperatively, and multimodality monitoring to help prevent POSCI occurrence, minimize secondary damage, and potentially improve the clinical outcome of after a POSCI. Further prospective cohort studies are needed to delineate incidence rate, current practice patterns for preventing injury and minimizing the clinical consequences of POSCI, factors that may increase the risk of POSCI, and determinants of clinical outcome in the event of a POSCI.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".