MétaCan
Menu
Back to cohort
Record W3151457843 · doi:10.1097/ede.0b013e31828c4663

Cell Phone Use and Crash Risk

2013· letter· en· W3151457843 on OpenAlexaboutno aff
David G. Kidd, Anne T. McCartt

Bibliographic record

VenueEpidemiology · 2013
Typeletter
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsPhoneMobile phoneCrashEpidemiologyPoison controlAffect (linguistics)EconometricsComputer scienceMedicineEnvironmental healthPsychologyTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

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

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0070.003

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.

Opus teacher head0.096
GPT teacher head0.375
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueEpidemiologySame topicHuman-Automation Interaction and SafetyFrench-language works237,207