Calls to the British Columbia Drug and Poison Information Centre: A summary of differences by health service areas
Bibliographic record
Abstract
OBJECTIVES: Poison control centres provide information on the management of poisoning incidents. The British Columbia (BC) Drug and Poison Information Centre recently implemented an electronic database system for recording case information, making it easier to use case data as a potential source of population-based information on health services usage and health status. This descriptive analysis maps poisoning case rates in BC, highlighting differences in patient age, substance type, medical outcome, and caller location. METHODS: There were 50,621 human exposure cases recorded during 2012 and 2013. Postal code or city name was used to assign each case to a Health Service Delivery Area (HSDA). Case rates per 1,000 person-years were calculated, including crude rates, age-standardized rates, age-specific rates, and rates by substance type, medical outcome, and caller location. RESULTS: The lowest case rate was observed in Richmond, a city where many residents do not speak English as a first language. The highest rate was observed in the Northwest region, where the economy is driven by resource extraction. Pharmaceutical exposures were elevated in the sparsely populated northern and eastern areas. Calls from health care facilities were highest in the Northwest region, where there are many remote Aboriginal communities. CONCLUSIONS: Case rates were generally highest in the primarily rural northern and eastern areas of the province. Considering these results alongside contextual factors informs further investigation and action: addressing cultural and language barriers to accessing poison centre services, and developing a public health surveillance system for severe poisoning events in rural and remote communities.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".