Tisseel and Its Effects on Wound Drainage Post-Thyroidectomy: Prospective, Randomized, Blinded, Controlled Study
Bibliographic record
Abstract
OBJECTIVE: This randomized, blinded, controlled study examines the effects of fibrin sealant (Tisseel, Laboratoire de production Baxter AG, Vienna, Austria) on wound drainage following thyroidectomy. METHODS: Fifty-six consecutive patients were enrolled in the study. Patients were randomized into Tisseel and non-Tisseel treatment groups. Wound drain output was tallied in 8-hour increments by observers blinded to the treatment groups. RESULTS: Fifty-six patients completed the study. Significant decreases in wound drainage were found in the first 8 hours in the Tisseel group. Eight hours postoperatively, wound output in the Tisseel group was reduced by 44% compared with the non-Tisseel group. A significant decrease in the total drainage over the 64-hour time period of 43% was noted between the treatment and control groups. Post-thyroidectomy wound drainage was reduced and trended to earlier drain removal. No significant changes in the length of hospital stay were noted, nor were postoperative complications encountered in either treatment group. CONCLUSIONS: Fibrin sealants offer a unique opportunity to safely decrease post-thyroidectomy wound drainage. This investigation furthers the evidence that fibrin sealants could safely enable the implementation of drain-free thyroidectomies.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".