The Learning Curve of Endoscopic Lumbar Interbody Fusion: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Endoscopic lumbar interbody fusion (ELIF) represents a key milestone in minimally invasive spinal surgery, offering reduced tissue trauma, lower complication rates, and faster recovery compared with open fusion. However, its steep learning curve remains a major barrier to widespread adoption. This systematic review aimed to synthesize current evidence on the ELIF learning curve and identify factors that influence the acquisition of surgical proficiency. Methods: A comprehensive literature search of PubMed, Embase, and the Cochrane Library was conducted for studies reporting quantitative analyses of the ELIF learning curve. Eligible articles included clinical data describing operative performance, complication rates, and learning curve cutoff points. Study quality was evaluated using the Newcastle–Ottawa Scale. Pooled data were analyzed to determine the mean cutoff point between the early and proficient phases and to compare outcomes across surgical approaches. Results: Five eligible studies encompassing 425 patients were included. Operative time was the most frequently assessed outcome, followed by hospital stay and complication rates. The pooled cutoff point for operative time was 23.4 ± 8.9 (range, 12–29) cases. Full-endoscopic ELIF tended to require longer operative times but resulted in shorter hospital stays than biportal techniques. Conclusions: ELIF reflects the evolution of endoscopic fusion techniques. The proficiency threshold varies according to the outcome parameters and the type of endoscopic system. Structured training programs and standardized educational pathways are essential for optimizing the learning process and ensuring safe and efficient implementation.
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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".