Anthropometric Measures, Recurrent Laryngeal Nerves Diameter, Electromyographic Responses and Vocal Cord Paralysis Risk
Bibliographic record
Abstract
OBJECTIVES: To examine (1) the relationship between anthropometric measurements and recurrent laryngeal nerve (RLN) diameter; (2) whether thin RLNs have different baseline electromyographic (EMG) characteristics; (3) if thin or branched morphology is associated with increased risk of EMG adverse events, loss of signal (LOS) or vocal cord paresis/paralysis (VCP). METHODS: In this prospective study, anthropometric data were collected, including weight, height, body mass index (BMI), neck circumference, shoulder diameter, and circumference of the right middle finger (RMF) or right ring finger (RRF). RESULTS: , body weight < 120 kg, height < 175 cm, neck circumference < 40 cm, shoulder diameter < 50 cm, and RMF or RRF < 7.6 cm (all p < 0.05). No statistically significant differences were detected in baseline EMG characteristics between thin and thick RLNs bilaterally (except for right RLN R2 latency). We had zero cases of LOS or VCP. In all branched nerves, motor fibers resided in the anterior branch. CONCLUSION: Multiple anthropometric factors were associated with RLN diameter. Most of the studied EMG signal characteristics did not statistically differ by RLN diameter. With zero events of LOS or VCP, our study is not powered to determine if thin diameter or branching are independent risk factors for RLN injury in a high-volume endocrine surgery practice utilizing IONM.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".