Casting Into The Future: Effectiveness of a 3D-Printed Fishhook Removal Task Trainer
Bibliographic record
Abstract
While participation in both recreational and commercial fisheries is common, it is not risk-free. Puncture wounds caused by fishhooks are commonly incurred by people who fish recreationally and commercially. Despite literature that details the challenges of treating fishhook injuries and specific techniques for fishhook removal, only a single publication focuses on teaching fishhook removal techniques to medical trainees and staff physicians. The aim of this technical report is to investigate the efficacy of using a 3D-printed task trainer for simulating and teaching fishhook removal techniques. To facilitate this, the 3D-printed Fishhook Emergency Removal Simulator (FISH-ER 3D) was designed by the Memorial University of Newfoundland (MUN) MED 3D Network and satellite research partner, Carbonear Institute for Rural Reach and Innovation by the Sea (CIRRIS). A sample of 22 medical residents and staff physicians were asked to evaluate the task trainer by way of a practical session, which was then followed by an evaluation survey. The overall realism of the 3D-printed task trainer components was ranked as "realistic" or "very realistic" by 86% of the evaluators. The majority of evaluators rated acquiring and performing various fishhook removal techniques using the simulator as "easy" or "somewhat easy". Most evaluators found that using the task trainer increased user competence and confidence with fishhook removal techniques, and 100% of the evaluators rated the task trainer as a "very valuable" or "valuable" training tool. The results of this report demonstrate support for the FISH-ER 3D as an efficacious simulator for building competence in fishhook removal techniques.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".