Clinical outcome measures following lateral versus posterior sacroiliac joint fusion: Systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Sacroiliac joint fusion (SIJF) is indicated in patients with chronic Sacroiliac joint (SIJ) pain or instability and is usually performed using minimally invasive techniques through lateral or posterior approach. Research question: Our study aims to compare the lateral approach to the posterior one in SIJF through meta-analysis of other studies. The outcome of each approach is measured using the visual analog scale (VAS), Oswestry disability index (ODI), or both. Materials and methods: The study was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Articles were extracted using Pubmed advance search till February 27th, 2023. Articles included were those limited to either lateral, posterior or both approaches. Articles written in a non-English language, case reports and smaller-than-three case series were excluded from the study. Risk of bias was assessed using the Newcastle-Ottawa and Jadad scales. Stata-17 software program was used for statistical analysis and creation of forest plots. Results: Forty-eight articles were available for the quantitative analysis, which represents a total of 2562 subjects. The average duration of postoperative follow-up was 21 months (3-72 months) and 17 months (6-72 months) for the VAS and ODI outcomes, respectively. The average percentage of improvement in the VAS was 57% (22-80%) in the lateral approach versus 58% (29-94%) in the posterior approach (p = 0.986). The average percentage of improvement in the ODI was 42% (11-75%) in the lateral approach versus 31% (11-65%) in the posterior one (p = 0.272). A trend towards performing posterior approaches more frequently was noted in studies published after 2017. Discussion and conclusion: Approach selection for SIJF depends mainly on patient's characteristics and surgeon's experience. Our study demonstrated no difference in VAS outcome between lateral and posterior approach. Lateral approach appeared to be superior in ODI outcome although not statistically significant. The main limitation of the study is the selection-bias as the majority of articles included were observational. Therefore, randomized procedural trials are needed to validate these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".