Lateral pterygoid muscle: A three‐dimensional analysis of neuromuscular partitioning
Bibliographic record
Abstract
The lateral pterygoid (LP) has been implicated in temporomandibular joint (TMJ) pathology. Few studies have examined muscle architecture of the superior (SLP) and inferior (ILP) heads of LP; moreover, the pattern of intramuscular innervation is poorly defined. The purpose of this study was to determine patterns of intramuscular innervation of LP using 3D modeling. The superior and lateral aspects of LP were exposed in 10 embalmed cadaveric specimens. Nerves entering the muscle, all branches of the mandibular nerve (V(3) ), were followed intramuscularly in short segments and sequentially digitized. Muscle volume, surrounding bone, and the TMJ disc were also digitized. The data were reconstructed into 3D models (Maya®) that were used to determine patterns of intramuscular innervation. It was found that the SLP had independent sources of innervation to each of the quadrants in its superior part (masseteric/posterior deep temporal/middle deep temporal/buccal) and one primary source of innervation (buccal) to the quadrants of the inferior part. This difference in innervation is significant as the superior part attaches to the TMJ disc-capsule complex, whereas the inferior part attaches to the mandibular condylar neck. Differing sites of attachment and sources of innervation for each part suggests that movement of the TMJ disc-capsule complex, independent of the condyle, may be possible. The buccal nerve supplied both the medial and lateral quadrants of the ILP, with the medial quadrants receiving additional innervation from V(3) muscular branches. Results of this study could be used to direct EMG/ultrasound studies of LP function as related to TMJ disorders.
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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 | 0.001 |
| 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) | 0.002 | 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".