Posterior to anterior spinal stiffness measured in a sample of 127 secondary care low back pain patients
Bibliographic record
Abstract
BACKGROUND: The sensation of spinal stiffness is a commonly reported symptom among back pain patients, with the clinical assessment of spinal stiffness usually being part of the decision-making process when deciding on providing manual treatment of low back pain. While any relationship between spinal stiffness and low back pain is likely to be multifactorial, prior exploration of this relationship has been overly simplistic (e.g., univariate regression analyses). The purpose of this study was to address this gap by taking a broader approach to compare instrumented measures of spinal stiffness to demographic characteristics, pain phenotypes, psychometrics, and spine-related disability in a sample of secondary care low back pain patients using multivariate regression analysis. METHODS: Instrumented spinal stiffness measures from 127 patients in secondary care were used to calculate terminal and global spinal stiffness scores. A best subset analysis was used to find the subsets of 14 independent variables that most accurately predicted stiffness based on the evaluation of the adjusted R-square, Akaike Information Criteria, and the Bayesian Information Criteria. FINDINGS: In the resulting multivariate models, sex (p < 0.001) and age (p < 0.001) were the primary determinants of terminal stiffness, while global stiffness was primarily determined by age (p = 0.003) and disability (p = 0.024). INTERPRETATION: Instrumented measures of spinal stiffness are multifactorial in nature, and future research into this area should make use of multivariate analyses.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".