Prevalence of multimorbid degenerative lumbar spinal stenosis with knee and/or hip osteoarthritis: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Lumbar spinal stenosis (LSS) and knee and hip osteoarthritis (OA) are prevalent conditions in the aging population and published literature suggests they share many symptoms and often are present at the same time in patients. However, no prevalence estimates of multimorbid LSS and knee and/or hip OA are currently available. The primary objective of this systematic review is therefore to estimate the prevalence of multimorbid LSS with knee and/or hip OA using radiological, clinical, and combined case definitions. METHODS: This systematic review protocol has been designed according to the guidelines from the Cochrane Collaboration and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols. A comprehensive search will be performed in the following databases: MEDLINE, EMBASE, CENTRAL, and CINAHL. Forward citation tracking will be performed in Web of Science. No restriction for publication date and language will be applied in the literature search, but only articles in English will be included. The search strategy will include the following domains: LSS, knee OA, and hip OA. Retrieved citations will be screened by two authors independently. Disagreements will be discussed until consensus, and a third reviewer will be consulted if consensus cannot be reached. Data extraction and assessment of risk of bias assessment will be done by two authors independently, using a standardized data extraction form and a modified risk of bias tool for prevalence studies. Meta-analysis estimating prevalence with 95% CI will be performed using a random effects model. Meta-regression analyses will be performed to investigate the impact of the following covariates: LSS clinical presentations, sample population, healthcare setting, risk of bias, and other patient characteristics on prevalence estimates for multimorbid LSS and knee and/or hip OA. DISCUSSION: The results of this review will provide the first estimates of the prevalence of multimorbid LSS and hip and knee OA based on various case definitions. The impact of covariates such as LSS clinical presentations, sample population, healthcare setting, risk of bias, and patient characteristics on prevalence estimates will also be presented. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, awaiting registration.
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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 | 0.073 | 0.119 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".