A method to repair the recurrent laryngeal nerve during thyroidectomy
Bibliographic record
Abstract
<h3>Summary</h3> Vocal cord palsy (VCP) is one of the most frequent complications following thyroidectomy. We evaluated the outcomes of intraoperative reconstruction of the recurrent laryngeal nerve (RLN). Of 917 patients who underwent thyroid surgery in a single high-volume general surgery ward between 2000 and 2015, 12 (1.3%) were diagnosed with RLN injury and were retrospectively categorized into 2 groups: group A (<i>n</i> = 5), with intraoperative evidence of iatrogenic transection or cancer invasion of the RLN, and group B (<i>n</i> = 7), with postoperative confirmation of VCP. In group A, immediate microsurgical primary repair of the RLN was performed. Postoperative assessment included subjective ratings (aspiration and voice quality improvement) and objective ratings (perceptual voice quality according to the grade, roughness, breathiness, asthenia and strain [GRBAS] scale, and direct laryngoscopy). In group A, roughness, breathiness and strain were significantly lower at 9 months than at 3 months (<i>p</i> < 0.05). Although larger, multicentre studies are needed, the results suggest potentially excellent postoperative phonatory function after immediate RLN reconstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".