New Model for Learning Ultrasound-Guided Needle to Target Localization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The acquisition of technical skills for the novice learner presents challenges for students and teachers alike. With the introduction of ultrasound techniques in regional anesthesia, there has been interest from residents, fellows, and staff to acquire the skills necessary to incorporate this technology into their everyday practice. However, as both ultrasound machines and commercial target models are inherently costly, there are often issues of accessibility that may affect the opportunity to learn the desired skills. METHODS: Readily available extra-firm tofu, wood dowel, and electrical wire are easily composed to create models for learning ultrasound-guided needle manipulation. RESULTS: Wood and wire targets embedded in tofu present hypo- and hyper-echoic targets that allow the learner to appreciate the relationship between the two-dimensional ultrasound screen image and three-dimensional target planes. CONCLUSIONS: This report presents an inexpensive, variable complexity model for learning ultrasound-guided needle-to-target localization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it