Highly Cited Works in Spinal Disorders
Bibliographic record
Abstract
STUDY DESIGN: Bibliometric analysis. OBJECTIVE: To identify, and summarize the key findings of, the top 100 most highly cited works published in spinal disorder topic-specific journals. SUMMARY OF BACKGROUND DATA: There is an abundance of published articles pertaining to spine surgery and spinal disorders. The number of citations a work receives provides a useful measure of its scientific impact. An understanding of the most cited works in spine surgery can identify literature that surgeons and researchers should be familiar with, point to the most active areas of research, inform the design of educational curricula, and help guide future research efforts. METHODS: Journals relating to spinal disorders were identified using the Journal Citation Reports database. We then searched the Web of Science database for articles appearing in each of these journals. The top 100 most cited works were selected for analysis. RESULTS: The top 100 most cited articles appeared in seven of eight journals dedicated to spinal disorders, with 84 in Spine and seven in the European Spine Journal. The citation count for individual articles ranged from 343 to 1949. Most works (73) were published between 1990 and 2004. The greatest number of articles (22) focused on low back pain, followed by biomechanics (13), degenerative disc disease (12), and lumbar spinal stenosis/lumbar fusion (9). With regard to study design, laboratory investigations were the most common (12), followed by guideline documents (11), reviews (10), and retrospective cohort studies (10). CONCLUSION: The most cited works in spinal disorder journals are guidelines for low back pain, descriptions of quality of life (QOL) metrics, or laboratory investigations into spinal biomechanics. A gap exists for work relating to neck pain or cervical spinal pathology (e.g., cervical myelopathy), representing opportunity for future work. Time of publication, topic of study, study design, and journal are possible determinants of likelihood of citation. LEVEL OF EVIDENCE: N/A.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.147 | 0.416 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".