Fetal and Newborn Exposure and Information Requests at the British Columbia Drug and Poison Information Centre in 2012/13
Bibliographic record
Abstract
Fetal and Newborn Exposure and Information Requests at the British Columbia Drug and Poison Information Centre in 2012/13Abstract Number:2706 Linda Dix-Cooper* and Tom Kosatsky Linda Dix-Cooper* British Columbia Centre for Disease Control, Canada, E-mail Address: [email protected] and Tom Kosatsky British Columbia Centre for Disease Control, Canada, E-mail Address: [email protected] AbstractWomen exposed to hazardous chemicals (e.g., heavy metals, poisonous gases, and pesticides) and drugs (e.g., prescription medications and alcohol) can unknowingly pass on these exposures to their infants during pregnancy via the placenta and after childbirth via breastfeeding. Early life exposures may lead to long-lasting developmental impairments, diseases, and death. Drug and Poison Information Centers (DPIC) usage rates among healthcare providers and the public by type of perinatal drug and chemical substance exposures and subsequent health outcomes have not been specifically quantified.We reviewed all requests to the British Columbia (BC) DPIC for fetal and newborn exposures through a keyword search using terms related to pregnancy and lactation from June 1, 2013–May 31, 2014. The number and types of requests received from healthcare providers and the public were summarized by type of substance and leading exposures and severity of associated health outcomes identified.We found that approximately 581 out of 30,535 requests received in 2013/14 were related to pregnancy and lactation-based perinatal exposures, after adjustment for misclassifications. Approximately half of all requests were from the public and 44% from healthcare providers. Among all perinatal exposures in the year 2013/14, the six most common substances were: drug information requests (n=84), analgesics (n=57), household cleaning substances (n=45), fumes/gases/vapours (n=45), vitamins (n=44), and food products/food poisoning (n=43). About 9.7% of requests involved moderate or major health effects. Overall, we calculate that approximately 1.3% of all women giving birth in BC may access DPIC services while pregnant or breastfeeding.Our study identifies emerging trends for specific substances of concern for infants of vulnerable pregnant and lactating women in BC and demonstrates the potential for real-time public health monitoring and surveillance using a large, rich DPIC database.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".