Lateral lumbar interbody fusion versus single position prone transpsoas approach: A comprehensive bibliometric analysis
Bibliographic record
Abstract
Background: Lateral lumbar interbody fusion (LLIF) and single position prone lateral (PTP) approaches represent significant advances in minimally invasive spine surgery, yet comprehensive comparative analysis of their research trajectories remains limited. Purpose: This bibliometric analysis aims to comprehensively compare the scientific landscapes surrounding LLIF and PTP approaches by examining publication trends, citation patterns, authorship networks, and thematic evolution. Methods: We systematically searched Scopus, PubMed and Google Scholar from inception through September 2025 for articles addressing LLIF and single position prone lateral approaches. Bibliometrix package in R was used to complete the bibliometric analysis. We included the following parameters: publication output, citation metrics, journal distribution, author productivity, international collaboration networks, keyword co-occurrence, and thematic mapping in the analysis. Results: We identified 823 LLIF publications and 107 single position prone lateral publications. LLIF accounted for 20,707 total citations with an h-index of 70 and a mean of 31.93 citations per document, whereas PTP accumulated 915 citations with an h-index of 17 and a mean of 8.44 citations per document. Although LLIF has a larger volume of publications, PTP has progressed more rapidly, showing annual growth rates of 44% compared with 30% for LLIF during comparable developmental periods. The United States contributed 45.6% of LLIF and 71.8% of PTP publications, with LLIF research spanning 42 countries and PTP 16 countries. Core research themes for both techniques evolved from technical descriptions to comparative effectiveness studies, and the substantial overlap in authors and institutions indicates complementary rather than competitive research trajectories. Conclusions: LLIF represents a mature surgical approach with a strong evidence base while PTP demonstrates accelerating research interest, with growth trajectories exceeding those of LLIF during comparable developmental stages. Based on current publication trends and thematic evolution, future research on PTP should prioritize rigorous comparative studies, long-term outcome evaluations, cost-effectiveness analyses, and patient-centered outcome research to strengthen the evidence base and guide clinical adoption.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.140 | 0.152 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".