Learning Curve of Uniportal Compared With Biportal Endoscopic Techniques for the Treatment of Lumbar Disc Herniation
Bibliographic record
Abstract
OBJECTIVES: Currently, unilateral biportal endoscopic (UBE) and uniportal full-endoscopic (UFE) techniques for the treatment of lumbar disc herniation (LDH) are gaining popularity. However, studies investigating the number of surgeries needed for surgeons to achieve proficiency in these procedures are lacking. This study aims to compare the early learning curve for UBE and UFE when treating LDH. METHODS: The learning curve for two fellowship-trained surgeons at our institution was retrospectively assessed for 160 consecutive patients (UFE: n = 100, UBE: n = 60) who underwent procedures between September 2020 and May 2023. Surgeon 1 first learned UBE, followed by UFE (S1BF), while Surgeon 2 first learned UFE and then UBE (S2FB). Operation time was evaluated as the primary outcome for determining the learning curve using cumulative sum (CUSUM) analysis. Secondary outcomes assessing endoscopic prowess include surgical outcomes, such as fluoroscopy usage times, postoperative hospital stays, the incidence of complications, and clinical outcomes, including visual analog scale (VAS) scores for back and leg pain, Oswestry disability index (ODI) score and modified MacNab criteria. RESULTS: The learning curve analysis identified the cutoff point in UBE at 14 cases and 11 cases for S1BF and S2FB, respectively, and in UFE at 31 cases and 27 cases, respectively. Without UFE or UBE experience, at the last follow-up, both the VAS back and leg pain in UFE were significantly higher than that in UBE (p < 0.05). Furthermore, the incidence of complications of UFE was also higher than that of UBE (29.0% vs. 7.1%). When surgeons have previous UFE or UBE experience, there was no significant difference in the clinical outcomes between UFE and UBE, and the complication rates were also similar (p > 0.05). After gaining UBE experience, the UFE performed by S1BF showed significantly better outcomes in fluoroscopy usage times (p = 0.024), surgical complications (p = 0.036), last follow-up VAS back pain (p = 0.003), and leg pain (p < 0.001) compared to S2FB. However, after gaining UFE experience, the S2FB only showed significant improvement in operation time (p = 0.041) during the process of learning UBE compared to S1BF. CONCLUSIONS: Regardless of whether UBE or UFE is learned first, both techniques can significantly shorten the learning curve for the other technique. We recommend prioritizing the learning of UBE. Compared with UBE, the learning curve for UFE was significantly steeper and longer with higher incidence of complications in the early stage.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".