Identifying optimal postmarket surveillance strategies for medical and surgical devices: implications for policy, practice and research
Bibliographic record
Abstract
BACKGROUND: Non-drug technologies offer many benefits, but have been associated with adverse events, prompting calls for improved postmarket surveillance. There is little empirical research to guide the development of such a system. The purpose of this study was to identify optimal postmarket surveillance strategies for medical and surgical devices. METHODS: Qualitative methods were used for sampling, data collection and analysis. Stakeholders from Canada and the USA representing different roles and perspectives were first interviewed to identify examples and characteristics of different surveillance strategies. These stakeholders and others they recommended were then assembled at a 1-day nominal group meeting to discuss and prioritise the components of a postmarket device surveillance system, and research needed to achieve such a system. RESULTS: Consultations were held with 37 participants, and 47 participants attended the 1-day meeting. They recommended a multicomponent system including reporting by facilities, clinicians and patients, supported with some external surveillance for validation and real-time trials for high-risk devices. Many considerations were identified that constitute desirable characteristics of, and means by which to implement such a system. An overarching network was envisioned to broker linkages, establish a shared minimum dataset, and support communication and decision making. Numerous research questions were identified, which could be pursued in tandem with phased implementation of the system. DISCUSSION: These findings provide unique guidance for establishing a device safety network that is based on existing initiatives, and could be expanded and evaluated in a prospective, phased fashion as it was developed.
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.
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 | 0.111 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".