Morphologic parameters of sacropelvic anatomy affecting spinal pathology
Bibliographic record
Abstract
Purpose of review The spine is intimately linked to the sacropelvis. Many studies have therefore emphasized the relationship between sacropelvic anatomy and spinal pathology. This article reviews the different methods proposed to assess sacropelvic anatomy. The clinical relevance of measuring sacropelvic anatomy also is discussed. Recent findings Of all parameters described in the literature to evaluate sacropelvic anatomy, pelvic incidence is the most widely used. New parameters describing the local anatomy of the sacrum and its orientation within the pelvis also could be useful to complement the evaluation of sacropelvic anatomy. Recent studies have assessed the influence of sacropelvic anatomy on the pathophysiology of different spinal disorders, but consistent findings have been reported mainly for spondylolisthesis. It is now recognized that sacropelvic anatomy is an important aspect for the complete evaluation of sagittal spinal alignment. Sacropelvic anatomy is strongly related to lumbar lordosis and its evaluation is paramount in surgical planning to determine optimal lumbar lordosis. Recent findings also suggest that sacropelvic anatomy should be assessed when considering surgical reduction in spondylolisthesis. Summary Assessment of sacropelvic anatomy is relevant for the evaluation and treatment of spinal pathology, especially when surgical treatment is contemplated.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".