Monitoring the use of anatomical teaching material using a low‐cost radio frequency identification system: A comprehensive assessment
Bibliographic record
Abstract
The correct tracking and monitoring of anatomical specimens is not only imperative in any modern body donation programs but also in any universities for which teaching the next generation of health care professionals is the primary mission. This has long been an arduous process for anatomy institutions across the world, and the recent focus of new curricula on self-directed learning adds new stress on specimens which are used by students. The radio frequency identification (RFID) technology has been proposed as a very effective tracking system in healthcare considering that it enables automatic identification and data capture of multiple items at once. In this study, the feasibility of a low-cost RFID inventory system is assessed, from its design to the performance of commercially available RFID tags in a gross anatomy laboratory. The results show that ultrahigh frequency-based RFID tags successfully performed when attached to a collection of 112 plastinated and 280 wet dissected specimens. Comparison analysis of different tags reveals, however, that careful selection of RFID tags needs to be considered when wet specimens need to be tracked as preservation fluids can absorb radio waves energy. This study demonstrates that it is economically feasible to incorporate RFID technology to closely monitor the use of anatomical teaching specimens. The described RFID inventory system was not only able to preserve the integrity of the specimens being used by limiting handling and therefore human error but was also able to identify missing or misplaced specimens and to update their status.
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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 | 0.000 |
| 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 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".