Applying Human Factors to the Procurement of Electrosurgical Medical Devices: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human factors evaluations are currently not conducted as part of the procurement process for medical devices in most hospitals. The complexity of medical devices and interactions between those devices, the working environment and the people who use them can create a high potential for errors. This study reports on the methods used to integrate human factors usability testing into the product evaluation of electrosurgical units (ESU's) prior to procurement. It also comments on the results of the various testing methods and the impact of the results on the final purchasing decision. The results of the human factors evaluations were used to make a purchasing decision for a major metropolitan hospital in Canada. A new purchase was necessary because the manufacturer was no longer supporting the product in use. Surprisingly, the product of choice was the oldest on the market with few new features. It was preferred and chosen based on usability and clinical acceptance by all users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it