Analysis of the Implications of Lumbopelvic Alignment on the Alignment of the Cervical Spine
Bibliographic record
Abstract
Objectives To date, standard patterns of alignment in the cervical spine have not been conclusively defined. Previous studies have attempted to establish these patterns. However, the results were inconsistent and limited by a small sample size. Knowledge of normal alignment is essential in evaluation and preoperative planning for deformity correction. This study aims to establish normative value groups for the alignment of the cervical spine. Methods Healthy volunteers (N = 441) between 20-40 years of age with no pre-existing spine pathology were recruited. Whole-body 2D EOS imaging was obtained for all participants. Using a semi-automated image segmentation software (KEOPS, by SMAIO), standard measures of alignment of the pelvis, lumbar, thoracic, and cervical spine were obtained. Coordinates of the cervical vertebral bodies, T1 and T2, were normalized on the centroid of T2 and input into a k-means algorithm to identify optimal clusters. A feature selection algorithm (recursive feature elimination) determined the best differentiating coordinates. Several machine learning models were trained, via a 5-fold cross validation, on the coordinates selected by the feature selection algorithm to predict labels defined by the k-means algorithm. Results The average age of participants was 28.2±5.1 years. Fifty-five percent of participants were female; the average BMI was 24.7±4.3 kg/m2. Three clusters were identified (n=176, 140, 125), best differentiated by C5 and C6 coordinates, with the mean coordinate points of each cluster displayed in Figure 1. The machine learning model with the highest accuracy was neural network, with accuracy = 94.4%, precision = 95.0%, recall = 94.4%, and f1 score = 94.4%. Figure 1: The mean coordinate points for each cluster. Cluster 2 has a kyphotic region around C5 while cluster 3 is purely lordotic. The red dots represent the centroids of each vertebra. Conclusion Three new clusters were identified based on alignment of the cervical spine, with the normative value for each cluster being the mean of each coordinate. Future directions include validating these clusters using an external dataset and examining the link between these clusters and alignment of the thoracolumbar spine and pelvis.
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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 | 0.001 | 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 | 0.001 | 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".