Detecting a signal in the noise: monitoring the global spread of novel psychoactive substances using media and other open‐source information
Bibliographic record
Abstract
OBJECTIVE: To determine the feasibility and utility of using media reports and other open-source information collected by the Global Public Health Intelligence Network (GPHIN), an event-based surveillance system operated by the Public Health Agency of Canada, to rapidly detect clusters of adverse drug events associated with 'novel psychoactive substances' (NPS) at the international level. METHODS AND RESULTS: Researchers searched English media reports collected by the GPHIN between 1997 and 2013 for references to synthetic cannabinoids. They screened the resulting reports for relevance and content (i.e., reports of morbidity and arrest), plotted and compared with other available indicators (e.g., US poison control center exposures). The pattern of results from the analysis of GPHIN reports resembled the pattern seen from the other indicators. CONCLUSIONS: The results of this study indicate that using media and other open-source information can help monitor the presence, usage, local policy, law enforcement responses, and spread of NPS in a rapid effective way. Further, modifying GPHIN to actively track NPS would be relatively inexpensive to implement and would be highly complementary to current national and international monitoring efforts.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".