Alternative Sources of Cautery in Thyroid Surgery and the Risk of Recurrent Laryngeal Nerve Injury: A Retrospective, Risk-Adjusted Analysis From the National Surgical Quality Improvement Program
Bibliographic record
Abstract
Objectives A risk-adjusted analysis was completed using data from the National Surgical Quality Improvement Program (NSQIP) to compare the rates of recurrent laryngeal nerve injury in thyroid surgery using traditional versus alternative sources of cautery (defined as Harmonic Scalpel © and LigaSure © ). Methods A retrospective cohort study was completed using the NSQIP database on adult patients who underwent total thyroidectomy, subtotal thyroidectomy, or completion thyroidectomy between 2016 and 2018. The primary outcome measure was recurrent laryngeal nerve injury. The exposure variable was use of conventional or alternative sources of cautery. Multivariable linear and logistic regression analyses were performed to control for potentially confounding variables. Results A total of 13,961 cases were analyzed; 9450 used alternative sources of cautery compared to 4511 where traditional cautery was used. There was no significant difference in rates of postoperative recurrent laryngeal nerve injury between the 2 sources of cautery compared. Conclusions Risk of recurrent laryngeal nerve injury should not be a factor when choosing method of cautery for thyroid surgery. Therefore, other factors like cost-effectiveness can be considered.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".