Applying Risk Management Principles to Medical Devices Performance Assurance Program—Defining the Process
Bibliographic record
Abstract
Biomedical Instrumentation & Technology 401 The management of medical devices entails a number of essential components. These include technology assessment, acquisition, inventory control, repair service, in-service education, performance assurance (PA), etc. The PA program, in some cases referred to as preventive maintenance (PM), deals with device operation, performance, and safety. In this paper, PM is regarded as a specific subcomponent or activity of the PA program. The PA program is defined as “a planned and scheduled method of performing inspections for performance verification, preventive maintenance, and safety testing.”1 In this context, performance verification (PV) entails testing according to a written procedure to ensure that equipment is performing within specified performance limits and PM is a planned periodic procedure for cleaning, lubricating, adjusting, and replacing components whose failure may impair equipment function. Safety testing (ST) in this context is performed to verify that equipment is in compliance with electrical safety requirements. Therefore, PA=PV+PM+ST. A similar equation was described by Ridgway2 but with slightly different terminology. In practice, performance assurance includes management of the program and development of test protocols/procedures.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".