The Nerve to Thyrohyoid Muscle as a Novel Donor Nerve for Laryngeal Reinnervation
Bibliographic record
Abstract
OBJECTIVES: Recurrent laryngeal nerve (RLN) injury may be a consequence of surgical procedures of the skull base, neck, and chest, with adverse consequences to function and quality of life. Laryngeal reinnervation offers a potentially stable improvement in vocal fold position and tone. The classic donor nerve is the ansa cervicalis, but is not always available due to damage or sacrifice during previous neck surgeries. Our objective was to introduce the nerve to the thyrohyoid (TH) muscle as an alternate donor nerve for reinnervation, which has not previously been described. METHODS: Case series of two patients using the TH nerve for laryngeal reinnervation after RLN injury, with description of surgical harvest. RESULTS: Follow-up results are available for 10 months (one patient) and 3 years (one patient) demonstrating both subjective and objective improvement in function. GRBAS scores were reduced. Maximal phonation time was improved. Patient rating of voice was stable or improved postoperatively. One patient described significant preoperative dyspnea which was significantly improved postoperatively, from a score of 24 to 10 out of 40 on the dyspnea handicap index. VHI was improved in one patient, but scores elevated in the other, despite a change from "moderately severe impairment" to "normal voice" subjectively. Neither patient experienced significant complications from the procedure. CONCLUSION: Laryngeal reinnervation procedures provide good outcomes in pediatric patients. When ansa cervicalis is not available as a donor nerve, the nerve to TH provides a reasonable alternative.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".