Tracing the evolving dynamics and research hotspots of spinal cord injury and surgical decompression from 1975 to 2024: a bibliometric analysis
Bibliographic record
Abstract
Background Exploration of the benefits and timing of surgical decompression in spinal cord injury (SCI) has been a research hotspot. However, despite the higher volume and increasing emphasis on quality there remains no bibliometric view on SCI and surgical decompression. In this study, we aimed to perform bibliometric analysis to reveal the core countries, affiliations, journals, authors, and developmental trends in SCI and surgical decompression across the past 50 years. Methods Articles and reviews were retrieved from web of science core collection between 1975 and 2024. The bibliometrix package in R was used for data analysis and visualizing. Results A total of 8,688 documents were investigated, indicating an ascending trend in annual publications. The USA and China played as the leaders in scientific productivity. The University of Toronto led in institutional productions. Core authors, such as Michael G. Fehlings, showed high productivity, and occasional authors showed widespread interests. Core journals like Spine and Spinal Cord served as beacons in this field. The interaction of core authors and international collaboration accentuated the cross-disciplinary feature of the field. Prominent documents emphasized the clinical significance of early decompression in 24 h post SCI. Conclusion Based on comprehensive bibliometric analysis and literature review, we identified the hotspots and future directions of this field: (1) further investigation into the molecular and cellular mechanisms to provide pre-clinical evidence for biological effects of early surgical decompression in SCI animal models; (2) further evaluation and validation of the optimal time window of surgical decompression based on large cohort, considering the inherent heterogeneity of subpopulations in complicated immune responses post SCI; (3) further exploration on the benefits of early decompression on the neurological, functional, and clinical outcomes in acute SCI; (4) evaluation of the optimal surgical methods and related outcomes; (5) applications of artificial intelligence-based technologies in spinal surgical decompression.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.140 | 0.184 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".