OP457 A Collaborative Horizon Scanning Alert For Disinvestment And Early Awareness
Bibliographic record
Abstract
Introduction In 2019, the Norwegian Institute for Public Health and Canadian Agency for Drugs and Technologies in Health (CADTH) received support from HTAi to produce a quarterly current awareness alert for the HTAi Disinvestment and Early Awareness Interest Group in collaboration with the HTAi Information Retrieval Interest Group. The alert focuses on methods and topical issues, and broader forecasts of potentially disruptive technologies that may be of interest to those involved in horizon scanning and disinvestment initiatives in health technology assessment (HTA). Methods Information specialists at both agencies developed search strategies for disinvestment and for horizon scanning in PubMed and Google. The template for the alert was based on an e-newsletter developed by the Information Retrieval Interest Group. Information specialists and researchers reviewed the monthly (PubMed) and weekly (Google) search results and selected potentially relevant publications. Additional sources were also identified through regular HTA and horizon scanning work. Results Alerts are posted quarterly on the HTAi Interest Group website; members receive an email notice when new alerts are available. While the revised PubMed searches are identifying relevant information, Google alerts have been disappointing, and this search may need to be revised further or dropped. When the one-year pilot project ends, in Fall 2020, interest group members will be surveyed to see if the alerts were useful, and whether they have suggestions for improving them. Conclusions Collaborating on this alert service reduces duplication of effort between agencies, and makes new research in horizon scanning and disinvestment more accessible to colleagues in other agencies working in these areas.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.225 | 0.105 |
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".