Comparing the Perceived Realism and Adequacy of Venipuncture Training on an in-House Developed 3D-Printed Arm With a Commercially Available Arm: Randomized, Single-Blind, Cross-Over Study
Bibliographic record
Abstract
Background: The venipuncture is one of the most frequently performed procedures in health care. Arm phantoms are available for training, because the procedure itself can be challenging. These phantom arms do not represent a realistic setting and do not offer opportunities to train challenging scenarios. Objective: This randomized, single-blind study aimed to train health care workers on both a commercially available injection arm and an in-house developed 3D-printed arm, to evaluate the perceived realism and adequacy of training on both arms. Methods: Participants were trained on both the commercially available arm (arm A) and the 3D-printed arm (arm B). Participants were randomized and blinded from knowing which arm they started training on. A questionnaire was filled in on, among others, the perceived realism of the arm (0 for not realistic, 100 for realistic) and adequacy of the training (inadequate, moderate, or adequate). Results: A total of 68 participants evaluated the perceived realism of arm A and B, which were scored on average 62.97 (SD 21.47) and 63.79 (SD 17.45), respectively. The difference in perceived realism of the two arms was not statistically significant (based on the paired t test, mean difference=-0.82, P=.78). Training on arm A was reported inadequate by 7% (5/68 participants), moderately adequate by 31% (21/68), and adequate by 62% (42/68). This was not significantly different from arm B (marginal homogeneity test, P=.74), with 4% (3/68), 38% (26/68), and 57% (39/68), respectively, reporting that the training was inadequate, moderately adequate, and adequate. Conclusions: The 3D-printed arm is as realistic and provides an equally adequate training compared to the commercially available arm. The 3D-printed arm offers the additional possibility to design different models representing several levels of difficulty for vascular morphology. This potentially lowers the number of venipuncture failures by preparing health care workers on challenging scenarios.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".