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Record W4317470390 · doi:10.1016/j.wnsx.2023.100152

A meta-analysis of risk factors for cage migration after lumbar fusion surgery

2023· review· en· W4317470390 on OpenAlexaboutno aff
Yang Hou, Haoyang Shi, Hongyang Shi, Tianyi Zhao, Jiangang Shi, Guodong Shi

Bibliographic record

VenueWorld Neurosurgery X · 2023
Typereview
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisCochrane LibraryInclusion and exclusion criteriaCageLumbarSurgeryIncidence (geometry)Systematic reviewData extractionMEDLINEInternal medicinePathologyBiology

Abstract

fetched live from OpenAlex

Objective: Cage migration is a rare complication after lumbar fusion surgery, and it is also the cause of lumbar revision surgery. Previous studies have reported that many influencing factors can increase the incidence of cage migration. However, there still remains controversial. The current study was conducted to investigate the risk factors influencing incidence of cage migration. Methods: A systematic database search of PubMed, Embase, Web of Science, Cochrane Library and Clinical Trials was performed for relevant articles published until July 2022. According to the inclusion and exclusion criteria, two evaluators independently conducted literature screening, data extraction and quality evaluation of the obtained literature. The Newcastle-Ottawa Scale (NOS) score was used for quality evaluation, and meta-analysis was performed by STATA 16.0 software. Results: A total of 2126 relevant articles were initially identified, and 7 articles were finally included in this study for data extraction and meta-analysis. The results of meta-analysis showed that the bony endplate injury, pear-shaped disc, and screw loosening are significantly correlated with cage migration. The OR values (95%CI) of the three factors were 7.170 (3.015, 17.051), 8.056 (4.050, 16.023), and 12.840 (3.570, 46.177) respectively. Conclusion: Bony endplate injury, pear-shaped disc, and screw loosening are the current risk factors for cage migration postoperatively.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.017
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.272
GPT teacher head0.382
Teacher spread0.110 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2023
Admission routes1
Has abstractyes

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