A collaborative pilot on current awareness alerts for disinvestment and horizon scanning
Bibliographic record
Abstract
OBJECTIVE: In 2019, members of the Health Technology Assessment international (HTAi) Interest Group for Disinvestment and Early Awareness (DEA-IG) and the HTAi Interest Group for Information Retrieval (IR-IG) agreed to produce quarterly current awareness alerts for members of the DEA-IG. The purpose was to pilot a predefined strategy for sharing new publications on methods and topical issues in this area. METHODS: Literature search strategies for PubMed and Google were developed. Retrieved citations were posted on the DEA-IG Web site. Members of the DEA-IG received an email notification when new alerts were available. An informal survey of the DEA-IG members was used to provide feedback after the pilot. RESULTS: Six alerts were issued during the pilot (June 2019-September 2020) with a total of 170 citations. The bulk of the information were 124 PubMed indexed citations, and of these, 96 were retrieved by the PubMed search strategies. Google searches were not found to be useful, but ongoing horizon scanning work at the Canadian Agency for Drugs and Technologies in Health (CADTH) provided additional information. Based on retrospective sorting, we considered thirty-five PubMed citations to be highly relevant for health technology assessment (HTA). The response rate to the survey was limited (seventeen respondents), but most respondents found the alerts useful for their work. CONCLUSIONS: The results of this pilot project can be used to revise search strategies and information sources, improve the relevance of the alerts, and plan for expanded dissemination strategies.
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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 | 0.084 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".