A proposed framework to improve the safety of medical devices in a Canadian hospital context
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
PURPOSE: Medical devices are used to monitor, replace, or modify anatomy or physiological processes. They are important health care innovations that enable effective treatment using less invasive techniques, and they improve health care delivery and patient outcomes. Devices can also introduce risk of harm to patients. Our objective was to propose a surveillance system framework to improve the safety associated with the use of medical devices in a hospital. MATERIALS AND METHODS: The proposed medical device surveillance system incorporates multiple components to accurately document and assess the appropriate actions to reduce the risk of incidents, adverse events, and patient harm. The assumptions on which the framework is based are highlighted. The surveillance system was designed from the perspective of a tertiary teaching hospital that includes dedicated hospital staff whose mandate is to provide safe patient care to inpatients and outpatients and biomedical engineering services. RESULTS: The main components of the surveillance system would include an adverse medical device events database, a medical device/equipment library, education and training, and an open communication and feedback strategy. Close linkages among these components and with external medical device/equipment networks to the hospital must be established and maintained. A feedback mechanism on medical device-related incidents, as well as implementation and evaluation strategies for the surveillance system are described to ensure a seamless transition and a high satisfactory level among the hospital staff. The direct cost items of the proposed surveillance system for consideration, and its potential benefits are outlined. CONCLUSION: The effectiveness of the proposed medical device surveillance system framework can be measured after it has been implemented in a Canadian hospital facility.
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 | 0.040 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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