Can active surveillance provide a rapid response to an emerging child health issue? The melamine example
Bibliographic record
Abstract
In mid-September 2008, the federal government became aware of an outbreak of renal disease (renal stones and/or acute renal failure) in very young children in China, associated with consumption of powdered infant formula that was adulterated with melamine. Thousands of children in China were hospitalized, and several died (1). Melamine contamination was also found in other Chinese products that had a milk component, such as candies and coffee drinks. Some Chinese milk-derived products sold in Canada were found to contain low levels of melamine and were recalled from the market (2). Infant formula manufactured in China is not approved for sale in Canada, and the manufacturers of infant formula sold in Canada do not use any milk ingredients originating in China. Therefore, the likelihood of Canadian infants being exposed to the formula that was affecting so many children in China was very low. Nevertheless, in light of patterns of international travel, adoption and immigration, there was a possibility that infants in Canada might be affected. Working in concert with Health Canada and the Canadian Food Inspection Agency, the Public Health Agency of Canada (PHAC) provided information for health care professionals and the public on this issue (3,4). The PHAC also endeavoured to determine whether there were cases of renal illness in Canadian children that may have been caused by the milk formula contamination in China.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".