Comparison of anterior and posterior approach in the treatment of acute and chronic cervical spinal cord injury: a meta-analysis
Bibliographic record
Abstract
Objective: A cervical spinal cord injury (CSCI) is a traumatic catastrophe that often leads to neurological dysfunction. The optimal surgical procedure for the treatment of CSCI remains debatable. The aim of this meta-analysis is to compare the neurological outcomes, complications, and clinical factors between anterior and posterior approach in CSCI treatment. Methods: We searched PubMed, Embase, Web of Science and Cochrane library from their inceptions to october 2023. Preoperative and postoperative Spinal Injury Association (ASIA) and Japanese Orthopedic Association (JOA) scores, and calculated recovery rates (RRs) were compared between the two strategies, and differences in complication rates, operation time, intraoperative blood loss and length of stay were also analyzed. Results: A total of five studies containing 613 patients were included, with 320 patients undergoing the anterior approach and 293 patients undergoing the posterior approach. Four of the studies included were retrospective cohort studies of high quality as assessed by the Newcastle Ottawa Scale. Additionally, there was one randomized controlled trial evaluated with the Cochrane Risk of Bias tool. Although both anterior and posterior approaches effectively facilitate spinal decompression and promote good neurological recovery, there was no significant difference in the incidences of neurological dysfunction and complications or other clinical features between the two approaches. Conclusion: There is no evidence thus far supports one approach over the other. Large-scale randomized controlled studies are warranted to further distinguish these two methods. Systematic Review Registration: https://www.crd.york.ac.uk/, PROSPERO [CRD42023438831].
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".