59 Injury ‘syndromics’: a proof-of-concept using detergent packets
Bibliographic record
Abstract
Purpose The purpose of this study is to demonstrate a proof-of-concept in using near real-time surveillance data to identify injuries resulting from new and emerging hazards. Approach Recently the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) moved from paper-based to online data collection format allowing for real-time data collection (eCHIRPP). As a proof-of-concept, eCHIRPP is being evaluated on the effectiveness of monitoring injuries relating to pre-packaged laundry detergent packets that were first introduced in Canada in 2011. Data from the eCHIRPP were extracted up to March 2014. Descriptive statistics were applied and linear regression was used to quantify trends. Results In total, 53 injury cases related to pre-packaged laundry detergent packets were recorded in eCHIRPP. The index case occurred in August of 2011, the same year the packets were first introduced. The number of cases increased in 2012 and 2013 to 19 and 31 cases respectively. Most injuries were to males (55%) and 92% of the cases were in children under the age of 5 years. While most of these injuries were occurring in basements and laundry rooms, some found children ‘playing’ with these pods in kitchens, family rooms, and hallways. The nature of injuries of most of these cases involved poisoning and toxic effects (57%) as well as injury to the eye (28%). Linear regression shows a positive trend with an increasing slope of 15 cases per year projected to result in 46 cases in 2014. Conclusions Real time data is an important tool for identification of new and emerging hazards. Significance and contributions Injury syndromics could be an important tool for identifying new opportunities for early prevention 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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".