Can the Mismatch of Measured Pelvic Morphology vs. Lumbar Lordosis Predict Chronic Low Back Pain Patients?
Bibliographic record
Abstract
Background: Measures of lumbar lordosis (LL) and elliptical modeling variables have been shown to discriminate between normal and chronic low back pain (CLBP) patients. Pelvic morphology influences an individual’s sagittal lumbar alignment. Our purpose is to investigate the sensitivity and specificity of lumbar sagittal radiographic alignment and modeling variables to identify if these can discriminate between normal controls and CLBP patients. Methods: We conducted a computer analysis of digitized vertebral body corners on lateral lumbar radiographs of normal controls and CLBP patients. Fifty normal controls were attained from a required pre-employment physical examination (29 men; 21 women; mean age of 27.7 ± 8.5 years), with no history of low back pain, a normal spinal examination, no pathologies, anomalies, or instability. Additionally, 50 CLBP patients (29 men; 29.5 ± 8 years of age) were randomly chosen and matched to the characteristics of the controls. The inclusion criteria required no abnormalities on lumbar spine radiographs. The parameters included the following: ARA L1-L5 lordosis, ARA T12-S1 lordosis, Cobb T12-S1, b/a elliptical modelling ratio, sacral base angle (SBA), and S1 posterior tangent to vertical (PTS1). Two measures of pelvic morphology were determined for each person—the angle of pelvic incidence (API) and posterior tangent pelvic incidence angle (PTPIA)—and the relationships between API − ARA T12-S1, API − Cobb T12-S1, and API − ARA L1-5 was determined. Descriptive statistics and correlations among the primary variables were determined. The receiver operating characteristic curves (ROC curves) for primary variables were analyzed. Results: The mean values of LL were statistically different between the normal and CLBP groups (p < 0.001), indicating a hypo-lordotic lumbar spine for the CLBP group. The mean b/a ratio was lower in the chronic pain group (p = 0.0066). The pelvic morphology variables were similar between the groups (p > 0.05). API had a stronger correlation to the SBA and Cobb T12-S1 than PTPIA did, while PTPIA had a stronger correlation to the S1 tangent and ARA T12-S1 than API did. While CLBP patients had a stronger correlation of ARA T12-S1 and Cobb T12-S1 relative to the pelvic morphology, they also had a reduced correlation of ARA L1-L5 lordosis relative to their SBA and pelvic morphology measures. API − T12-S1, API − L1-L5, and API − Cobb T12-S1 were statistically different between the groups, p < 0.001. Using ROC curve analyses, it was identified that ARA L1-L5 lordosis of 36° and ARA T12-S1 of 68° have a good sensitivity and specificity to discriminate between normal and CLBP patients. ROC curve analyses identified that lordosis ARAT12-S1 < 68° (AUC = 0.83), lordosis ARAL1-L5 < 36° (AUC = 0.78), API − ARA T12-S1 < −18° (AUC = 0.75), API − ARAL1-L5 > 35° (AUC = 0.71), and API − Cobb T12-S1 < −5° (AUC = 0.69) had moderate to good discrimination between groups (AUC = 0.83, 0.78, 0.75, and 0.72). Conclusions: Pelvic morphology is similar between normal and CLBP patients. CLBP patients have an abnormal ‘fit’ of their API − ARAT12-S1 and L1-L5 lumbar lordosis relative to their pelvic morphology and sacral tilt shown as a hypolordosis.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".