In Vivo Measurement of Lumbar Facet Joint Area in Asymptomatic and Chronic Low Back Pain Subjects
Bibliographic record
Abstract
In Brief Study Design. In vivo measurement of lumbar facet joint surface area. Objective. To investigate lumbar facet joint surface area in relation to age and the presence of chronic low back pain. Summary of Background Data. Facet joint surface area is an important parameter for understanding facet joint function and pathology, but information on the lumbar facet joint is limited, especially in relation with age and low back pain symptoms. Methods. In vivo measurements of the lumbar facet joints (L3/L4-L5/S1) were performed on 90 volunteers (57 asymptomatic subjects and 33 chronic low back pain subjects) using subject-based 3-dimensional facet joint surface computed tomography models. Results. The facet joint surface area increased significantly at each successive inferior level. In the low back pain subjects aged >40 years, both superior and inferior facet surface areas increased except superior facets at L5/S1 compared with younger subjects. In the asymptomatic subjects aged >40 years, only the superior facets showed an increase in the L3/4 facet surface area compared with younger subjects. Conclusion. The lumbar facet areas measured in vivo in this study were similar to previous cadaveric studies. The lumbar facet area was significantly greater at the inferior lumbar levels and also increased with age. This age-related increase in the facet joint surface was observed more in the low back pain subjects compared with asymptomatic subjects. The increase in the area of the facet joint surface is probably secondary to increased load-bearing in the lower lumbar segments and facet joint osteoarthritis. The lumbar facet joint surface areas were measured in vivo in asymptomatic and chronic low back pain subjects using subject-based 3-dimensional facet joint surface computed tomography models. The facet joint surface area increased significantly at each successive inferior level. The area increased with age, especially in the low back pain subjects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".