The Use of Preliminary Scientific Evidence in Public Health: A Case Study of XMRV
Bibliographic record
Abstract
The rapid response to XMRV as a novel pathogen has highlighted some challenges pertaining to policy making and editorial responsibilities in a policy environment influenced by the precautionary principle.N Once published, preliminary scientific evidence can result in rapid changes in policy and can undergo widespread dissemination via both the Internet and social media. N The impact on policy and the propagation of the initial scientific informationmay not cease if the evidence is disproven and retracted from peer-reviewed journals.N Regulators should consider the use of frameworks to guide the use of the precautionary principle and a separate, more flexible policy stream for precautionary policies.N Editors should continue to develop strategies to place preliminary scientific evidence of potential public health relevance in context for the public and for policy makers.Policy Forum articles provide a platform for health policy makers from around the world to discuss the challenges and opportunities in improving health care to their constituencies.
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 | 0.203 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.021 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".