Total intravenous anesthesia vs inhaled anesthetic for intraoperative visualization during endoscopic sinus surgery: a double blind randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Bleeding during endoscopic sinus surgery (ESS) can impair visualization and delay surgical progress. The role that anesthetic technique may have on the quality of surgical field during ESS has been previously studied. However, meta-analyses have deemed the current literature inconclusive and lacking methodological consistency. This study was designed with these critiques in mind to assess the effect of total intravenous anesthesia (TIVA) vs inhaled anesthetic on the quality of the surgical field during ESS. METHODS: This study was a double-blind, randomized, controlled trial of 30 patients of American Society of Anesthesiologists (ASA) class 1 or 2 undergoing bilateral ESS for the primary diagnosis of chronic rhinosinusitis. In addition to standard techniques to minimize blood loss, study patients were randomized to maintenance anesthesia with intravenous propofol or inhaled desflurane. Anesthetic depth was standardized using bispectral index (BIS). The primary outcome measured was the Wormald grading scale to assess the endoscopic surgical field. RESULTS: The use of TIVA was associated with a statistically significant reduction in mean Wormald score compared to desflurane (4.21 vs 5.53, p = 0.024). Mean Boezaart score was also lower in the TIVA arm (2.18 vs 2.76, p = 0.034). Experimental groups were homogeneous in all compared baseline characteristics. Secondary outcomes including surgical duration, time to extubation, and estimated blood loss were not found to be statistically significant between experimental groups. CONCLUSION: Even with all other factors implemented to optimize the surgical field, utilization of TIVA vs inhaled anesthetic still resulted in a statistically significant improvement in surgical field during ESS.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".