Canadian poison control centres: preliminary assessment of their potential as a resource for public health surveillance
Bibliographic record
Abstract
CONTEXT: In the United States (US) and Europe, surveillance based on calls to poison control centres has identified new hazards and evolving exposure trends. In Canada, the value of poison control centre calls as a tool for health hazard surveillance is largely unrecognized. OBJECTIVES: This preliminary survey was undertaken to describe current operational characteristics and surveillance capacities at Canadian poison control centres and to determine potential for developing a Canadian poison control centre collaborative network. METHODS: A structured quantitative-qualitative survey was administered to medical directors and clinical supervisors at the five Canadian poison control centres between March and May, 2012. RESULTS: All five Canadian poison control centres operate 24/7 with each serving more than one province/territory. Annual call volumes range from 10,000 to 58,000. Data analysis is limited to detection of previously unrecognized hazards and short-term event-based adverse health monitoring. Currently no centre maintains systematic ongoing collection, integration and analysis of data. Constraints on personnel, resources and funding were identified as barriers to increasing capacity to provide and analyse call data. CONCLUSIONS: The potential exists to use Canadian poison control data as a novel source of public health surveillance. That they serve as sentinels for new or unexpected exposure events, have real-time electronic call-record capacity and demonstrate an interest in developing and sharing their call-record information supports their integration into existing public health networks.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".