An Investigation of the Association between 3D Spinal Alignment and Fibromyalgia
Bibliographic record
Abstract
Fibromyalgia syndrome (FMS) is a common condition lacking strong diagnostic criteria; these criteria continue to evolve as more and more studies are performed to explore it. This investigation sought to identify whether participants with FMS have more frequent and larger postural/spinal displacements in comparison to a matched control group without the condition of FMS. A total of 67 adults (55 females) out of 380 participants with FMS were recruited. Participants with FMS were sex- and age-matched with 67 asymptomatic participants (controls) without FMS. We used a three-dimensional (3D) postural assessment device (Formetric system) to analyze five posture variables in each participant in both groups: (1) thoracic kyphotic angle, (2) trunk imbalance, (3) trunk inclination, (4) lumbar lordotic angle, and (5) vertebral rotation. In order to determine whether 3D postural measures could predict the likelihood of a participant having FMS, we applied the matched-pairs binary logistic regression analysis. The 3D posture measures identified statistically and clinically significant differences between the FMS and control groups for each of the five posture variables measured (p < 0.001). For three out of five posture measurements assessed, the binary logistic regression identified there was an increased probability of having FMS with an increased: (1) thoracic kyphotic angle proportional odds ratio [Prop OR] = 1.76 (95% CI = 1.03, 3.02); (2) sagittal imbalance Prop OR = 1.54 (95% CI = 0.973, 2.459); and (3) surface rotation Prop OR = 7.9 (95% CI = 1.494, 41.97). We identified no significant probability of having FMS for the following two postural measurements: (1) coronal balance (p = 0.50) and (2) lumbar lordotic angle (p = 0.10). Our study’s findings suggest there is a strong relationship between 3D spinal misalignment and the diagnosis of FMS. In fact, our results support that thoracic kyphotic angle, sagittal imbalance, and surface rotation are predictors of having FMS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".