Prevalence and risk factors for cage subsidence after lumbar interbody fusion
Bibliographic record
Abstract
INTRODUCTION: Lumbar interbody fusion (LIF) is an effective treatment for lumbar degenerative diseases. Cage subsidence (CS) contitutes one of the most common postoperative complications. Many risk factors for CS after LIF have been reported in some studies. However, controversies still exist. The objective of this study will be to summarize data on the prevalence and risk factors of CS after LIF. METHODS AND ANALYSIS: Our study present a protocol that conducted a systematic review and meta-analysis of prevalence and risk factors for CS after LIF. Two reviewers retrieved the relevant articles using the 5 databases (PubMed, Scopus, EMBASE, Cochrane Library, and Web of Science) from inception to May 31st, 2021. Primary outcome will be the prevalence of CS after LIF. Second outcomes include the risk factors associated with postoperative CS and clinical outcomes associated with postoperative CS. Three reviewers will screen citation titles and abstracts and evaluated full-text of each potentially relevant citation, and then extracted the data using a data extraction form. Any discrepancies in decisions between reviewers will be resolved through discussion. We assessed the methodological quality and risk of bias of the included studies based on the Newcastle-Ottawa Quality Assessment Scale (NOS). The aim of the extra analysis is to explore the explanations of the heterogeneity (age, gender, race, year of publication, type of study and surgical procedure). Publication bias will be assessed by Begg test, Egger test and funnel plots. ETHICS AND DISSEMINATION: No primary data will be collected and individual patient information and endangering participant rights, thus ethics approval is not required. Findings will be reported through publication and media. PROTOCOL REGISTRATION NUMBER: PROSPERO CRD42021257981 (https://www.crd.york.ac.uk/PROSPERO/#joinuppage).
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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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".