Fusion <i>in situ</i> versus reduction for spondylolisthesis treatment: grading the evidence through a meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: During surgical procedure on lumbar spondylolisthesis, the role of reducing slip remains controversial. The purpose of the present study was to compare fusion in situ with reduction in clinical and radiographic outcomes. METHODS: A literature research was performed at PubMed, Embase, Web of Science, and Cochrane Library. After screening by two authors, ten articles were brought into this meta-analysis finally, and the quality was evaluated by the modified Newcastle-Ottawa Scale (NOS). Isthmic, moderate, and serious spondylolisthesis were all analyzed separately. Sensitivity analyses were performed for high-quality studies, and the publication bias was evaluated by the funnel plot. RESULTS: Most criteria did not have statistical differences between reduction and fusion in situ groups. However, in reduction group, the union rate was significantly higher (P=0.008), the slippage was much improved (P<0.001) and the hospital stay was much shorter comparing to no-reduction group (P<0.001). Subgroup analysis (containing moderate and serious slip, or isthmic spondylolisthesis) and sensitivity analysis were all consistent with original ones, and the funnel plot indicated no obvious publication bias in this meta-analysis. CONCLUSIONS: Both reduction and fusion in situ for lumbar spondylolisthesis were related with good clinical results. Reduction led to higher rate of fusion, better radiographic slippage, and shorter hospital stay. After sufficient decompression, reduction did not incur additional risk of neurologic impairment compared with fusion in situ.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it