Mapping horizon scanning systems for medical devices: similarities, differences, and lessons learned
Bibliographic record
Abstract
OBJECTIVES: This article presents the mapping of horizons scanning systems (HSS) for medical devices, conducted by the Medical Devices Working Group of the International Horizon Scanning Initiative (IHSI MDWG). It provides an overview of the identified HSS, highlights similarities and differences between the systems, and lessons learned. METHODS: Potentially relevant HSS were identified through literature searches, scan of an overview of EuroScan members, and input from the IHSI MDWG members. Structured information was collected from organizations that confirmed having an HSS for medical devices. RESULTS: Sixteen initiatives could be identified, of which 11 are currently ongoing. The purposes of the HSS range from raising awareness of trends and new developments to managing informed decisions on innovative health services in hospitals. The time-horizon is most often 3 years up to a few months before market entry. Three models of identification of new technologies crystallized: a reactive (stakeholders outside HSS inform), a pro-active (actively searching multifold sources), and a hybrid model. Prioritization is often conducted by separate committees via scoring or debate. The outputs focus either on in-depth information of single technologies or on a class of technologies or on technologies in specific disease areas. CONCLUSIONS: The identified HSS share the common experience that horizon scanning (HS) for medical devices is a resource-intensive exercise that requires a dedicated and skilled team. Insights into the identified HSS and their experiences will be used in the continued work of the IHSI MDWG on its proposal for an IHSI HSS for medical devices.
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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 | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".