A digital atlas for ultrasound guided nerve blocks of the upper limb
Bibliographic record
Abstract
Ultrasound guidance (UG) ameliorates the dependency on surface landmarks to perform nerve blocks by providing real‐time imaging of subcutaneous tissues, allowing for purposeful needle movements to accurately deliver anesthetic. UG has been associated with improvements in the quality of sensory blocks, the onset time, and the success rate. The objective of this project is to create a digital atlas (DA) to aid trainees with their interpretation of ultrasound (U) anatomy, so that benefits of UG are actualized. Surface anatomy, serial cadaver dissections, U, and Visible Human Project images have been integrated into the DA to illustrate relationships between surface landmarks, physical anatomical relationships, and the planar U image. Each block and its indications are described in text and subsequently demonstrated in narrated videos. The DA includes an introductory module on mechanics and operation of the U device. Feedback solicited at monthly anesthesiology resident training will guide the design of the DA as content for trunk and lower limb blocks is developed. We anticipate that having online access to the DA will allow residents to acquire the requisite foundation of knowledge to interpret U anatomy. This would allow training programs to efficaciously use limited lab time to reinforce key concepts and develop students procedural skills (needle/ultrasound probe manipulation) through kinesthetic learning. Grant Funding Source : Ontario Graduate Scholarship
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".