The Sound of Safety: DIVOT (Doppler Imaging for Vascular Orientation in Thoracic Procedures) Protocol
Bibliographic record
Abstract
Each year, more than 200,000 thoracentesis and percutaneous chest tube thoracostomy procedures are performed in the United States [1-4]. In both procedures, the initial step involves advancing a needle over the superior aspect of the rib into the intercostal space to access the pleural cavity. Traditional teaching suggests that this technique avoids the neurovascular bundle, which is typically shielded by the inferior border of the rib. However, this technique does not guarantee safety. Computed tomography studies have shown that the intercostal arteries (ICAs) are highly tortuous, with positions that can vary significantly within the intercostal space [5-7]. This variability can lead to ICA laceration even with an optimal traditional technique [8-9]. Significant hemorrhage into the pleural space may initially go unnoticed but can progress to hemorrhagic shock or even tension hemothorax physiology [10-12]. Improved procedural guidance is needed to enhance safety and achieve the goal of zero patient harm. We propose the DIVOT (Doppler Imaging for Vascular Orientation in Thoracic procedures) protocol using a combination of high-frequency linear ultrasound, color, and Power Doppler (PD) to identify an ICA and its collaterals before needle insertion. This can reduce the risk of accidental vascular injury during thoracentesis or percutaneous chest tube thoracostomy.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.024 |
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".