Clinical Characteristics and Risk Factors of Hyperkyphosis in a Chinese Cohort With Axial Spondyloarthritis: A Multicenter Retrospective Study
Bibliographic record
Abstract
Objective This study aimed to investigate the clinical characteristics and risk factors of hyperkyphosis in a Chinese cohort of patients with axial spondyloarthritis (axSpA). Methods A cross-sectional study was conducted in a group of 607 patients with axSpA attending 12 hospitals across 11 centers from March 2022 to March 2024. Univariate and multivariate logistic regression analyses were used to explore the relevant risk factors of hyperkyphosis. A nomogram model was used for impact factor visualization and Spearman correlation analysis was used to analyze the relationship between the risk factors. Results Multivariate logistic regression revealed that male sex, disease duration, patient global assessment, erythrocyte sedimentation rate, modified Stoke Ankylosing Spondylitis Spine Score (mSASSS), pharmacological treatment, and Axial Spondyloarthritis Disease Activity Score based on C-reactive protein significantly influenced hyperkyphosis (allP< 0.05). Based on the results of the multivariate logistic regression analysis, we constructed a nomogram model for clinical evaluation with an area under the curve of 0.98 and an accuracy of 0.95. Spearman correlation analysis showed a positive correlation between the Assessment of SpondyloArthritis international Society Health Index (ASAS HI) and mSASSS (ρ 0.16,P< 0.001), whereas pharmacological treatment was negatively correlated with disease activity and mSASSS (ρ –0.24 and –0.18, respectively;P< 0.001). Conclusion Controlling disease activity in clinical practice is crucial. Active pharmacological treatment should be employed to delay radiological progression and to improve patient ASAS HI scores, psychological well-being, and physical functioning. Additionally, smoking cessation and weight control are recommended to reduce disability.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".