Surgical vs ultrasound-guided drainage of deep neck space abscesses: a randomized controlled trial: Surgical vs ultrasound drainage
Bibliographic record
Abstract
INTRODUCTION: Deep neck space abscesses (DNAs) are relatively common otolaryngology-head and neck surgery emergencies and can result in significant morbidity with potential mortality. Traditionally, surgical incision and drainage (I&D) with antibiotics has been the mainstay of treatment. Some reports have suggested that ultrasound-guided drainage (USD) is a less invasive and effective alternative in select cases. OBJECTIVES: To compare I&D vs USD of well-defined DNAs, using a randomized controlled clinical trial design. The primary outcome measure was effectiveness (length of hospital stay (LOHS) and safety), and the secondary outcome measure was overall cost to the healthcare system. METHODS: Patients presenting to the University of Alberta Emergency Department with a well-defined deep neck space abscess were recruited in the study. Patients were randomized to surgical or US-guided drainage, placed on intravenous antibiotics and admitted with airway precautions. Following drainage with either intervention, abscess collections were cultured and drains were left in place until discharge. RESULTS: Seventeen patients were recruited in the study. We found a significant difference in mean LOHS between patients who underwent USD (3.1 days) vs I&D (5.2 days). We identified significant cost savings associated with USD with a 41% cost reduction in comparison to I&D. CONCLUSIONS: USD drainage of deep neck space abscesses in a certain patient population is effective, safe, and results in a significant cost savings to the healthcare system.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 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".