Can Product-Specific Assurance Case Templates Be Used as Medical Device Standards?
Bibliographic record
Abstract
International standards are a key ingredient in the quality assurance of software-intensive medical devices. One problem with such standards is that they often describe a lifecycle process that should be used to develop the system, rather than describe acceptance criteria to be applied to the system itself, thus guaranteeing safety directly in terms of the artefact's attributes. In the past few years, the U.S. Food and Drug Administration (FDA) introduced a (strong) recommendation that manufacturers submit an assurance case in their submission for approval to market an infusion pump. This reflects a move toward a more product/evidence-based approach to certification, compared with the primarily process-based certification used in the past. The perceived advantage of an assurance case is that it obliges the manufacturer to make an explicit argument regarding the safety/security/reliability of their product, under expected operating conditions. Taking this idea one step further, we explore whether there are benefits to using an assurance case Template as a new kind of standard, replacing existing process standards, and we describe some benefits of doing this.
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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 | 0.131 | 0.346 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".