International neuromonitoring study group guidelines 2018: Part II: Optimal recurrent laryngeal nerve management for invasive thyroid cancer—incorporation of surgical, laryngeal, and neural electrophysiologic data
Bibliographic record
Abstract
The purpose of this publication was to inform surgeons as to the modern state-of-the-art evidence-based guidelines for management of the recurrent laryngeal nerve invaded by malignancy through blending the domains of 1) surgical intraoperative information, 2) preoperative glottic function, and 3) intraoperative real-time electrophysiologic information. These guidelines generated by the International Neural Monitoring Study Group (INMSG) are envisioned to assist the clinical decision-making process involved in recurrent laryngeal nerve management during thyroid surgery by incorporating the important information domains of not only gross surgical findings but also intraoperative recurrent laryngeal nerve functional status and preoperative laryngoscopy findings. These guidelines are presented mainly through algorithmic workflow diagrams for convenience and the ease of application. These guidelines are published in conjunction with the INMSG Guidelines Part I: Staging Bilateral Thyroid Surgery With Monitoring Loss of Signal. Level of Evidence: 5 Laryngoscope, 128:S18-S27, 2018.
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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 | 0.037 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".