Alternative sources of cautery may improve post-operative hematoma rates but increase operative time in thyroid surgery
Bibliographic record
Abstract
A retrospective risk-adjusted analysis was completed using data from the National Surgical Quality Improvement Program (NSQIP) to (1) compare the risks of post-operative hematoma for thyroid surgery using conventional cautery compared alternative energy devices (defined as LigaSure and Harmonic Scalpel), and (2) compare operative times for the same. The primary outcome variable was post-operative hematoma occurrence. The secondary outcome variable was operative time. The exposure variable was use of conventional or alternative sources of cautery. All adult patients who underwent a total thyroidectomy, subtotal thyroidectomy or completion thyroidectomy between 2016 and 2018 were included. Multivariable linear and logistic regression analyses were performed to control for potentially confounding variables. A total of 13,330 cases were analyzed; 4342 used conventional cautery, and 8988 used alternative sources. There was a statistically significant decrease in post-operative hematoma risk using alternative sources of cautery compared to conventional cautery (OR 0.75; 95% CI 0.58-0.98) (p = 0.04). Use of alternative sources of cautery added 4.95 min onto operative time (95% CI 2.45-7.45) which was statistically significant (p < 0.0001). After controlling for confounding variables, there was a statistically significant lower rate of post-operative hematoma in thyroidectomies performed using alternative sources of cautery compared to those performed with traditional hemostatic methods. Alternative sources of cautery increased operative time by 4.95 min.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".