Principal component analysis of the relationship between pelvic inclination and lumbar lordosis
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to describe the relationship between pelvic inclination (PI) and lumbar lordosis (LL). Pelvic inclination and pelvic tilt are two different names for the same metric. The geometrical parameters of the spine and pelvis were measured using surface topography scanning, and the data was explored for any physical relationships using principal component analysis.Once widely assumed to be a direct correlation, research in the 1980s first cast doubt upon the PI to LL relationship. And yet, other studies have suggested a relationship does exist. Decades later, the rehabilitation professionals often still rely on this supposed correlation when making decisions about rehabilitation treatment interventions. This theoretical relationship requires further clarification, which is explored herein. METHODS: Surface topography imaging is a technology that has proven to be a radiation-free way to produce accurate, reliable skeletal alignment measures. Patient data from one physical rehabilitation clinic was collected at the time of initial assessment. Patients presented with a wide range of musculoskeletal complaints. Surface topography scans were performed on 107 patients at the commencement and completion of their therapy. Principal component analysis was performed on the collected data to determine how these spine and pelvic alignment parameters changed between the two points in time and what trends and/or relationships exist between the parameters. Our analysis evaluated eight spinal and pelvic measurements as input and focused on LL and PI as the two principal components at time points of beginning and completion of treatment. RESULTS: Pelvic inclination and lumbar lordosis changed during treatment but were not correlated. CONCLUSION: Our data demonstrates that pelvic inclination and lumbar lordosis do not have a predictable relationship as previously assumed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".