Partial Neurolysis of the Hypoglossal Nerve for Selective Lingual Atrophy in a Porcine Model
Bibliographic record
Abstract
OBJECTIVES: Obstructive sleep apnea in children is most commonly treated with adenotonsillectomy; however, in cases of significant tongue base prolapse, this may prove inadequate. Surgical procedures used to increase the retroglossal airway have significant morbidities and low patient acceptability. We theorized that effective reduction of the tongue base can be achieved through partial denervation of the hypoglossal nerve, which is easily accessed in the submandibular space with minimal morbidity. METHODS: We performed a prospective, experimental study in which topographic innervation maps of porcine tongue were generated by stimulating the hypoglossal main trunk and peripheral branches. The effects of complete unilateral nerve sectioning on tongue base volume and linear dimensions were measured and compared to the contralateral control side. In the final stage, only the peripheral nerve that was determined as the main supply to the tongue base was sectioned, and the results were compared to those in the matched controls. RESULTS: A medial branch of the hypoglossal nerve was consistently identified as the main motor supply to the tongue base. Complete denervation resulted in a measurable decrease in the volume of the tongue base as compared to that of controls. Partial neurolysis produced inconsistent changes in 2 subjects, with decreases in linear dimensions that were not proportional to the decrease in volume. Histologically, complete denervation was followed by a significantly greater replacement of muscle with fat and connective tissue as compared with partial neurolysis. CONCLUSIONS: There were inconsistent changes in volume and linear dimensions of the tongue base following partial neurolysis of the hypoglossal nerve over the 3-month experimental period.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".