Biomarkers related to hypertrophy of the ligamentum flavum: a systematic review of the literature
Bibliographic record
Abstract
OBJECTIVE: Spinal stenosis is one of the most common spinal disorders in the elderly. Hypertrophy of the ligamentum flavum (HLF) can contribute to spinal stenosis. The current literature suggests that various biomarkers may play important roles in the pathogenesis of HLF. However, the connection between these biomarkers and the development of HLF is still not well understood. This systematic review aims to explore the current literature on biomarkers related to the development of HLF. METHODS: A literature search was conducted using PubMed, Embase, Web of Science, and Cochrane Library. The search strategy looked for the titles, abstracts, and keywords of studies that contained a combination of the following phrases: "ligamentum flavum OR yellow ligament," "biomarkers," and "hypertrophy." Recorded data included study design, demographic characteristics (number of patients of each gender and mean age), study period, country where the study was conducted, biomarkers, and diagnostic modalities used. Risk of bias was assessed using the Newcastle-Ottawa Scale for case-control studies. RESULTS: The authors identified 39 studies. After screening, 26 full-text original articles assessing one or more biomarkers related to HLF were included. The included studies were conducted over a 22-year period. The most popular biomarkers studied, in order of frequency reported, were collagen types I and III (n = 10), transforming growth factor β (TGF-β) (n = 8), and interleukin (IL)-6 (n = 6). The authors found that mechanical stretching forces, tissue inhibitor of metalloproteinases 2 (TIMP-2) induction, and TGF-β were associated with increased amounts of collagen I and III. IL-6 expression was increased by microRNA-21, as well as by leptin, through the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway. CONCLUSIONS: Biomarkers such as TGF-β, IL-6, and collagen I and III have been consistently correlated with the development of HLF. However, the pathogenesis of HLF remains unclear due to the heterogeneity of the studies, patient populations, and research at the molecular level. Further studies are necessary to better characterize the pathogenesis of HLF and provide a more comprehensive understanding of how these biomarkers may aid in the diagnosis and treatment of HLF.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".