The Use of Extremely Anthropomorphized Artefacts in Medicine
Bibliographic record
Abstract
Anthropomorphized, or human-like artefacts, have been used in teaching and training of medical skills for a long time. The most famous artefact used today is probably the Resusci ® Anne CPR training manikin, which is used for training of resuscitation skills. However, what has changed over the lifespan of these artefacts is the level of human-like features in them. All around the globe, highly anthropomorphized ICT artefacts are used in training of medical skills. Amongst others, the UNAM University in Mexico City and Royal North Shore Hospital in New South Wales use artefacts, which are closer to human-like robots than traditional manikins in teaching. The purpose of this article is to look deeper into this phenomenon, consider its potential implications for the patient-physician relationship and quality of patient care, and to propose some practical methods for minimizing the possible risks emerging from the use of these extremely anthropomorphized artefacts.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".