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Enregistrement W2180984799 · doi:10.1093/aje/kwv257

Re: “Associations of Body Mass Index, Smoking, and Alcohol Consumption With Prostate Cancer Mortality in the Asia Cohort Consortium”

2015· letter· en· W2180984799 sur OpenAlexaff
Rachel A. Murphy, Trevor Dummer, Carolyn Gotay

Notice bibliographique

RevueAmerican Journal of Epidemiology · 2015
Typeletter
Langueen
DomaineMedicine
ThématiqueLiver Disease Diagnosis and Treatment
Établissements canadiensCanadian Centre for Applied Research in Cancer ControlUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésBody mass indexMedicineCohortAlcohol consumptionProstate cancerCohort studyEnvironmental healthOncologyDemographyCigarette smokingGerontologyCancerInternal medicineAlcohol

Résumé

récupéré en direct d'OpenAlex

Fowke et al. (1) recently reported null associations between several risk factors for prostate cancer (body mass index (BMI; weight (kg)/height (m)2), smoking, and alcohol consumption) and prostate cancer mortality across 6 countries in southern and eastern Asia. The authors concluded that the lack of association they found casts doubt on the validity of these risk factors and that differences in prostate cancer mortality between Asian and Western populations may reflect variation in prostate cancer screening practices. The accompanying commentary (2) focused on the impact of screening on risk factors for cancer and suggested that understanding the etiology of cancers may be best accomplished through the study of populations without widespread screening. We agree with both sets of authors about the importance of assessing the impact of screening on cancer outcomes. However, concluding that the previously identified risk factors have limited utility is premature in the absence of high-quality data on these risk factors and exposures. We suggest that inadequate assessment of potential risk factors is an alternative explanation for the observed null associations between BMI, smoking, and alcohol consumption and prostate cancer in southern and eastern Asia. One significant limitation of the study by Fowke et al. is that data on risk factors in the Asia Cohort Consortium were collected only at baseline, whereas cancer surveillance occurred over decades in some of the cohorts. Regarding tobacco, smoking status was limited to never smoking versus ever smoking at baseline. These available data could not identify how prostate cancer risk may have changed with changes in smoking behavior such as cessation. It is well-known that the risk of developing lung and other types of cancer decreases with smoking cessation and continues to decrease with more tobacco-free years (3). The BMI analysis presents an additional challenge. Although current World Health Organization BMI cutoff points are used for international classification of underweight, overweight, and obesity, there is considerable debate over interpretation of BMI cutoffs in Asian populations (4), with many authors suggesting that determination of overweight and obesity should be made at lower BMI levels in Asian populations (5, 6). Thus, the “healthy” reference BMI range of 22.5–24.9 in this study may have included persons with BMI-associated health risks and may have obscured associations between overweight/obesity and prostate cancer. Furthermore, as Fowke et al. mentioned in the Discussion section of their paper (1), the most consistent relationships between alcohol consumption and prostate cancer have been shown at higher levels of consumption than were present in the Asia Cohort Consortium (5). Thus, their analysis did not provide a basis for drawing conclusions about this potential risk factor. Lastly, it is important to consider the endpoint when assessing the impact of risk factors. For diseases with long latency periods and high survival rates such as prostate cancer, incidence rather than mortality may be a more appropriate endpoint for identifying etiological indicators (6). Prostate cancer mortality reflects the severity of the cancer, therapies received, and additional factors that may be independent of those that are linked with disease incidence. In summary, given the limitations of their data set, it is not surprising Fowke et al. found null associations (1). We suggest that this study demonstrates the need for better measurement of potential etiological variables to advance our understanding of the roles of both modifiable lifestyle risk factors and screening in the prevention and early detection of prostate cancer. R.A.M. was an employee of DSM Nutritional Products (Parsippany, New Jersey) from 2014 to 2015. DSM Nutritional Products was not involved with any aspect of this publication, and R.A.M. does not have existing relationships with DSM.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,037
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,044
Score d'incertitude au seuil0,029

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,037
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,002
Communication savante0,0040,003
Science ouverte0,0030,002
Intégrité de la recherche0,0440,044
Charge utile insuffisante (le modèle a refusé de juger)0,0080,013

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.

Tête enseignante Opus0,073
Tête enseignante GPT0,382
Écart entre enseignants0,308 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2015
Routes d'admission1
Résumé présentnon

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