59 Injury ‘syndromics’: a proof-of-concept using detergent packets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<h3>Purpose</h3> 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. <h3>Approach</h3> 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. <h3>Results</h3> 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. <h3>Conclusions</h3> Real time data is an important tool for identification of new and emerging hazards. <h3>Significance and contributions</h3> Injury syndromics could be an important tool for identifying new opportunities for early prevention efforts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it