Digital Atlas for Ultrasound‐Guided Regional Anesthesia Nerve Blocks of the Trunk
Bibliographic record
Abstract
Ultrasound is a more recent tool anesthesiologists use to provide regional anesthesia. Ultrasound‐guided regional anesthesia has the following benefits: allows for the real time imaging of target nerves and surrounding structures, is used independently of surface landmarks, facilitates purposeful needle movements, improves the quality of sensory block, the onset time, the success rate compared to neurostimulation, and uses fewer needle attempts for nerve localization, which reduces the risk of nerve injury. The purpose of this project is to create a digital atlas of the sensory innervation of the trunk, to help trainees improve their sonoantomy interpretation. The atlas will include cadaver dissections, Visible Human Project images and surface anatomy to demonstrate the relationship between ultrasound images and the corresponding surface and internal anatomy. The blocks and their indications will be described in text and demonstrated visually with a narrated video. The atlas will include the following blocks: thoracic epidural, spinal, paravertebral, intercostal, ilioinguinal, transversus abdominis plane, transversalis fascia block, and the rectus sheath block. The atlas will be available online for trainees prior to practical training sessions in the lab. Future projects would test the efficacy of the atlas and look for ways to improve the teaching methods of ultrasound‐guided regional anesthesia. Grant Funding Source : N/A
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.028 |
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".