Harmonic scalpel impact on blood loss and operating time in major head and neck surgery: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Long operating time and high blood loss contribute to post-surgical morbidity. Therefore, strategies to reduce these factors should to be tested using robust methods. The purpose of this study was to evaluate the impact of using the harmonic scalpel on operating time and blood loss in patients undergoing resection for advanced oral cancer (OSCC). METHODS: Thirty-six adult head and neck cancer patients with advanced OSCC requiring primary tumor resection with uni- or bi- lateral selective neck dissection from July 2012 to September 2014 were randomized to either the control group (traditional surgery) or the experimental group (harmonic surgery). Patients older than 18 years who were able to provide informed consent were eligible. Primary outcomes of interest were: intraoperative blood loss (mL) and operative time (minutes) for the ablative part of the surgery. RESULTS: Mean blood loss in the experimental group was 260 mL versus 403 mL in the control group (p = 0.08). Mean operative time was 140 min in the experimental group and 159 min in the control group (p = 0.2). CONCLUSIONS: In this randomized controlled trial, use of the harmonic scalpel did not effect intraoperative blood loss or OR time in patients undergoing surgery for advanced OSCC. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02017834 .
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".