Perk Tutor: An Open-Source Training Platform for Ultrasound-Guided Needle Insertions
Bibliographic record
Abstract
Image-guided needle placement, including ultrasound (US)-guided techniques, have become commonplace in modern medical diagnosis and therapy. To ensure that the next generations of physicians are competent using this technology, efficient and effective educational programs need to be developed. This paper presents the Perk Tutor: a configurable, open-source training platform for US-guided needle insertions. The Perk Tutor was successfully tested in three different configurations to demonstrate its adaptability to different procedures and learning objectives. 1) The Targeting Tutor, designed to develop US-guided needle targeting skills, 2) the Lumbar Tutor, designed for practicing US-guided lumbar spinal procedures, and (3) the Prostate Biopsy Tutor, configured for US-guided prostate biopsies. The Perk Tutor provides the trainee with quantitative feedback on progress toward the specific learning objectives of each configuration. Configurations were implemented through simple rearrangement of hardware and software components, attesting to the modularity and ease of configuration. The Perk Tutor is provided as a free resource to enable research and development of educational programs for US-guided intervention.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".