Bibliographic record
Abstract
The author responds: Dr. Kidd1 agrees that previous epidemiological studies on the risk ratio of cell phone conversations while driving were biased because they did not take into account the proportion of time spent not driving during a control period (ie, part-time driving).2 Dr. Kidd’s first objection1 to the adjustment method is that the 2005–6 Seattle Global Positioning System (GPS) data2 are a different time and place than the epidemiological studies. An analysis3 of 2007–8 Chicago GPS data yielded similar results to Seattle,2 supporting the robustness of such data. His second objection1 is that the two prevalence estimates4,5 of phone conversation while driving are inconsistent. This is a misreading. The prevalence of 6.7%4 is for a 24-hour prevalence period to match the 24-hour period in the GPS driving consistency analysis in my original article.2 The prevalence of 11%5 is for a daytime prevalence period in my subsequent analysis5 to match the daytime hours in the epidemiological studies. Kidd’s third objection1 is that small differences (eg, in average call duration) might produce important changes in the adjusted risk ratio. Such variations make it preferable to use an adjustment method that is valid for any average call duration. The Table accomplishes this by calculating a rate ratio (RR) from the Toronto study6 raw caller counts and window durations.TABLE: Crude and Adjusted RR Calculations for the Toronto Study6The crude RR is 4.59, which incorrectly assumes that subjects were in their cars during an entire 10-minute control window (1700 total person-minutes). However, subjects were likely in their cars during only 20% of a prior-day control window.5 To avoid this part-time driving bias, control-window in-car person-minutes were adjusted to 340 (Table). Let ρ be the ratio of out-of-car to in-car control-window caller rates (ρ does not depend on call duration, window duration, or in-car time). The number of callers in the in-car control window is readily solved as 37/(4ρ + 1). For portable phones, ρ is estimated as 0.1,4 yielding 26.4 in-car control callers and adjusted RR 1.29.5 For embedded phones, ρ is 0, yielding 37 in-car control callers and adjusted RR 0.92, within the 95% confidence interval of the OnStar embedded phone with RR 0.62 (0.37–1.05).7 Future studies should report the number of people conversing on cell phones before crashes and in control periods only while driving. It is clear that previous epidemiological estimates of cell phone conversation producing a relative risk about 4 times greater than without cell phone conversation are wrong. ACKNOWLEDGMENTS I thank Joshua Cohen, Charlene Hallett, Linda Angell, Katja Kircher, Greg Fitch, and Michael Posner for helpful comments and Sean Seaman for programming assistance in analyzing the GPS datasets. Richard A. Young Department of Psychiatry and Behavioral Neurosciences Wayne State University School of Medicine Detroit, MI [email protected]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.022 |
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; both teacher heads agree on what is shown here.
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".