Three‐dimensional study of the musculotendinous architecture of lumbar multifidus and its functional implications
Bibliographic record
Abstract
Abstract Lumbar multifidus (LMT) is a key muscle, which provides stability to the lumbar spine, and has been shown to have altered neuromuscular recruitment following acute episodes of low back pain. Architectural parameters are important determinants of function, but have not been well documented for LMT. Therefore, the purpose of this study is to model and quantify the architecture of LMT throughout its volume. Nine male and one female formalin‐embalmed cadaveric specimens (average age 80 ± 11 years) without any evidence of spinal deformity/pathology were used. The musculotendinous components of LMT were serially dissected and digitized. Next, the data were imported into MAYA™ to create a three‐dimensional model of each segment of LMT from which architectural parameters including fiber bundle length (FBL), fiber bundle angle (FBA), and tendon length were quantified. Water displacement was used to determine volume. The data were analyzed using paired t ‐tests and ANOVA followed by Tukey's post‐hoc test ( P ≤ 0.05). LMT (L1–L4) has three architecturally distinct regions: superficial, intermediate, and deep. Intermediate LMT was absent in all specimens at L5. Mean FBL decreased significantly ( P ≤ 0.05) from superficial (5.8 ± 1.6 cm) to deep (2.9 ± 1.1 cm) as did volume (superficial, 5.6 ± 2.3 ml; deep, 0.7 ± 0.3 ml) measured at each region. By contrast, mean FBA increased from superficial to deep. The current study lends further evidence to support the role of different regions within LMT to serve distinct functions particularly to produce movement and/or control stability. Clin. Anat. 21:539–546, 2008. © 2008 Wiley‐Liss, Inc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".