Tisseel to Reduce Postparotidectomy Wound Drainage: Randomized, Prospective, Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Tisseel (Baxter Corp. Ontario, Canada) is a fibrin-based tissue glue that has been widely used to reduce wound drainage, achieve hemostasis, and decrease surgical complications. To date, Tisseel has not been evaluated in a randomized prospective trial for use in parotid surgery. OBJECTIVES: To determine whether the use of Tisseel in parotidectomy decreases postoperative wound drainage, the duration of percutaneous drainage, the length of hospital stay, and the frequency of complications. METHODS: Sixty consecutive parotidectomy patients were randomized into two groups: a group treated with 2 cc of Tisseel prior to wound closure and a control group. Postoperative wound drainage was measured for all patients by blinded hospital staff. The duration of percutaneous drainage, duration of hospital stay, and incidence of complications at the 3-week follow-up were assessed. RESULTS: A statistically significant difference in total drainage volume (p < .02) and frequency of postoperative seroma (p < .05) was demonstrated between patients treated with Tisseel prior to wound closure and the control group. CONCLUSION: The use of Tisseel in parotidectomy patients prior to wound closure significantly decreases total drainage volume and the frequency of postoperative seroma.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".