ArticleJournal of the American Academy of Orthopaedic Surgeons. Global research & reviews2022
Surgeon Learning Curve and Clinical Outcomes of Minimally Invasive Anterior Lumbar Interbody Fusion With Posterior Percutaneous Instrumentation.
Article in Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Evolution of Alignment and Clinical Outcomes During One Surgeon's Learning Curve in L5-S1 Anterior Lumbar Interbody Fusion: A Single-Center Experience.Journal of clinical medicine · 2026Article
- Evolving Paradigms in Spinal Surgery: A Systematic Review of the Learning Curves in Minimally Invasive Spine Techniques.Neurospine · 2024Article
- A novel technique for decortication of the lumbar facet joints for posterolateral fusion with percutaneous exposure: A cadaveric feasibility study.World neurosurgery: X · 2024Article
- Perspective: How can risks to patients be limited during spine surgeons' learning curves?Surgical neurology international · 2024Review
- Stroke pathway performance assessment: a retrospective observational study.BMC health services research · 2023Observational
- Vaccines as treatments for prostate cancer.Nature reviews. Urology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study reports the learning phase of the minimally invasive anterior lumbar interbody fusion (mini-ALIF) approach with a vascular and orthopaedic spine surgeon team.
methodsAdult patients who underwent primary mini-ALIF at the lowest two segments of the lumbar spine (i.e., L4/5, L5/S1) between January 2010 and December 2018 were analyzed.
resultsOne hundred twenty-seven patients were included. There was no notable change in total surgical time over the study period. Estimated blood loss markedly decreased until stabilizing at case 30 and slowly declined thereafter. The mean estimated blood loss was 184 mL for L5/S1, 232 mL for L4/L5, and 458 mL for two-level mini-ALIF. There were 20 vascular issues requiring primary repair or packing. Vascular issues declined over time, with a rate of 32% in the first 25 cases and 0% in the last 25. The postoperative complication rate was highest in the first 25 cases (7 of 21 total complications). The odds ratio of vascular injury with body mass index (BMI) > 35 was 4.09 (1.4 to 11.7 confidence interval, P ≤ 0.008). Total surgical time and postoperative complications increased with increasing BMI.
conclusionPerformance of the mini-ALIF approach is associated with a learning curve of 25 to 30 cases before complications begin to decline. BMI > 35 is associated with increased surgical time and complications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.