Prone Positioning of Patients with Cervical Spine Pathology
Bibliographic record
Abstract
Abstract Patients with cervical trauma or degenerative disease often require surgical decompression and stabilization in the prone position and are at the risk of secondary neurological injury during this transfer. This review aims to explore the current literature on different methods of positioning patients prone and to identify the safest technique to achieve prone positioning in patients with an unstable cervical spine undergoing posterior cervical spine surgery. We searched the Embase, Medline, and Medline-in Process databases for literature in English related to prone positioning patients with cervical spine pathology undergoing spine surgery. Seventy-three citations were identified as relevant and reviewed in detail with 20 articles being identified as answering the clinical questions posed. Our literature review identified three methods of prone positioning patients with cervical pathology: logroll with manual in-line stabilization (MILS), rotating the patient on a specialized spinal table using a “sandwich and flip” technique, and awake prone positioning. Each of these methods has its own advantages and disadvantages. When comparing the degree of neck movement between positioning techniques, “sandwich and flip” rotation was associated with over 50% reduction in both flexion–extension and axial–lateral rotation as compared to logroll with MILS. Awake self-positioning of a patient is another alternative that allows for rapid neurological assessment after repositioning. A “sandwich and flip” is the safest way to turn a patient with cervical pathology into a prone position for surgery. For cooperative patients, who are physically capable, awake self-positioning is a good alternative.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".