Bibliographic record
Abstract
To the Editor: Young1 attempted to correct for a potential bias in early epidemiological studies of cell phone use and crash risk. Concerns with his analyses have been raised, including an incorrect assumption that people talk on phones only while driving. In response, Young2,3 conducted two reanalyses that substantially revise his original correction method by (1) refining the estimates of driving inconsistency and (2) adding a new correction ratio to account for the prevalence of phone use during periods of driving and nondriving. Based on these reanalyses, Young concludes there is no increase in crash risk with phone use (adjusted risk ratio = 1.292 and 1.13). However, his reanalyses are based on data that are not comparable with the epidemiological study samples, which greatly affect the validity of his corrected estimates. The new correction ratio of Young2,3 is based on data from several studies of US drivers and wireless subscribers. These studies provide reasonable estimates of cell phone use in the United States, but not necessarily the epidemiological study populations. Young’s data on US phone use are much more recent (2009–2010) than the epidemiological study data (1994–1995 and 2002–2004), even though cell phone use has changed over time. Moreover, handheld cell phone use was prohibited during the more recent epidemiological study, but not in the earlier Canadian study or in most US states. Evidence shows that handheld cell phone bans reduce use during driving. Even small inaccuracies in Young’s estimates can greatly affect conclusions drawn from the correction ratio. For example, Young estimates the average duration of cell phone conversations at 1.17 minutes using 2009–2010 US data,4 but the same data show that the average cell phone conversation during the more recent epidemiology study period was 2.95 minutes. When the latter value is used in Young’s correction ratio equation without other changes, the adjusted crash risk ratio increases from 1.29 to 2.5. Compounding possible problems with the precision of Young’s estimates is that some are inexplicably inconsistent in his two reanalyses. He uses different estimates of the prevalence of phone use while driving (11% and 6.7%), driving consistency (20% and 15%), and total hours in the day when a phone could be used (11 and 24 hours). In conclusion, Young is correct that estimates of crash risk associated with phone use from early epidemiological studies may not have accounted sufficiently for driving inconsistency. His original correction to these estimates was flawed, and his revised corrections exacerbate rather than remove these flaws. ACKNOWLEDGMENTS We thank our colleague David Zuby who contributed with helpful feedback. David G. Kidd Insurance Institute for Highway Safety Arlington, VA [email protected] Anne T. McCartt Insurance Institute for Highway Safety Arlington, VA
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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".