Reliability of the Biomechanical Assessment of the Sagittal Lumbar Spine and Pelvis on Radiographs Used in Clinical Practice: A Systematic Review of the Literature
Bibliographic record
Abstract
Background: Biomechanical analysis of the sagittal alignment of the lumbar spine and pelvis on radiographs is common in clinical practices including chiropractic, physical therapy, scoliosis-related thoraco-lumbo-sacral orthosis (TLSO) management, orthopedics, and neurosurgery. Of specific interest is the assessment of pelvic morphology and the relationship between angle of pelvic incidence, sacral slope, and lumbar lordosis to pain, disability, and clinical treatment of spine conditions. The current state of the literature on the reliability of common methods quantifying these parameters on radiographs is limited. Methods: The objective of this systematic review is to identify and review the available studies on the reliability of different methods of biomechanical analysis of sagittal lumbo-pelvic parameters used in clinical practice. Our review followed the recommendations of the preferred reporting items for systematic reviews and meta-analyses (PRISMA). The design of this systematic review was registered with PROSPERO (CRD42023379873). Results: The search strategy yielded a total of 2387 articles. A total of 1539 articles were screened after deduplication and exclusion by automation tools, leaving 473 full-text articles that were retrieved. After exclusion, 64 articles met the inclusion criteria. The preponderance of the evidence showed good to excellent reliability for biomechanical assessment of sagittal lumbo-pelvic spine alignment. Conclusions: The results of this systematic review of the literature show that sagittal radiographic analysis of spinal biomechanics and alignment of the human lumbo-pelvic spine is a reliable tool for aiding diagnosis and management in clinical settings.
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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 | 0.032 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".