“The future should not take us by surprise”: Preparation of an early warning system in Denmark
Bibliographic record
Abstract
OBJECTIVES: To explore and test methods for the operation of a national Early Warning System (EWS) in Denmark and to support decision making by the Danish Centre for Evaluation and Health Technology Assessment on this issue. METHODS: On the basis of literature reviews, information from members of EuroScan, and supported by clinical experts and stakeholders, existing methods were adapted and new methods were developed as part of a feasibility study. RESULTS: Approximately 200 technologies in 30 specialties were identified on the basis of information by EuroScan. A new instrument was developed to distinguish between important and unimportant technologies (filtering). Clinical experts in six specialties applied the instrument to sixty-two technologies in their respective fields, of which nine (15%) were judged potentially important for the Danish health care system. For priority setting, adapting a Dutch instrument to the Danish context was discussed. In principle, the instrument was acceptable, but several changes were proposed, for example, relating to the decentralized structure of the Danish health care system. For early assessment, the format and methods applied by SBU and Canadian Coordinating Office for Health Technology Assessment (CCOHTA) were compared and applied to pharmaceuticals (glitazones in treatment of type 2 diabetes mellitus) and a procedure (embolization of uterine fibromas). Given the main target group of the Danish EWS, local decision makers, the CCOHTA format was preferred. CONCLUSIONS: The findings of the study have laid the foundation for an EWS using appropriate methods adapted to local circumstances. On the basis of the findings, a decision was made to start an EWS.
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 | 0.124 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".