Cellular Telephones and Motor Vehicle Collisions: Some Variations on Matched Case-Control Analysis
Why this work is in the frame
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Bibliographic record
Abstract
We describe the analysis of some matched pair binary data arising from a study designed to investigate whether cellular telephones are associated with motor vehicle collisions. Conditional and random effects approaches to the problem are derived and compared. Driving intermittency is a potential confounder, and its effect is assessed by strategic choices of the control period, and by application of the bootstrap. Donald A. Redelmeier Department of Medicine University of Toronto and Division of Clinical Epidemiology Sunnybrook Health Sciences Centre and Robert Tibshirani Department of Preventive Medicine and Biostatistics and Department of Statistics University of Toronto 1 Introduction In this paper we carry out a comparative analysis of some matched paired binary data, using both the standard conditional analysis and also a random effects analysis. We analyze a dataset that arose from a study designed to investigate whether cellular telephones were associated with motor vehicl...
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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 | 0.001 |
| Science and technology studies | 0.001 | 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