Notice bibliographique
Résumé
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]
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,050 | 0,022 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».