The Utility of the Harmonic Scalpel in Selective Neck Dissection
Bibliographic record
Abstract
OBJECTIVES: To determine the impact of the harmonic scalpel on intraoperative blood loss and operative time in selective neck dissection (SND) (levels I-IV) for head and neck squamous cell carcinoma (HNSCC). STUDY DESIGN: Prospective randomized controlled trial. SETTING: A single, tertiary care institution (Foothills Medical Centre) in Calgary, Alberta, Canada. SUBJECTS: A total of 31 patients (36 neck dissections) were prospectively enrolled between January 2009 and March 2010. METHODS: Patients were randomized to receive a neck dissection with either the harmonic scalpel or the traditional technique of using electrocautery and sharp dissection. The study included adult patients older than age 18 years diagnosed with HNSCC and who required an SND (levels I-IV). Study exclusion criteria included previous treatment for head and neck cancer and all patients unwilling or unable to provide informed consent. Primary clinical outcomes were intraoperative blood loss and operative time. Secondary outcomes included intraoperative complications and surgical drain output. RESULTS: Intraoperative blood loss was significantly lower in the harmonic scalpel group compared to the traditional group (158 vs 61 mL, P = .02). There was no difference in operative time (81 minutes harmonic vs 85 minutes traditional) or total drain output (at both 48 hours and 1 week) between the groups. There were no intraoperative complications reported in either group. CONCLUSIONS: Results from this study suggest that the harmonic scalpel can reduce blood loss during SND for HNSCC. The harmonic scalpel had no impact on operative time, postoperative drain output, or complication rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".