Notice bibliographique
Résumé
We congratulate Hyuna Sung and colleagues1Sung H Siegel RL Rosenberg PS Jemal A Emerging cancer trends among young adults in the USA: analysis of a population-based cancer registry.Lancet Public Health. 2019; 4: e137-e147Summary Full Text Full Text PDF PubMed Scopus (244) Google Scholar for their analyses of cancer trends among young adults in the USA.1Sung H Siegel RL Rosenberg PS Jemal A Emerging cancer trends among young adults in the USA: analysis of a population-based cancer registry.Lancet Public Health. 2019; 4: e137-e147Summary Full Text Full Text PDF PubMed Scopus (244) Google Scholar The authors observed an increase in the incidence of several obesity-related cancers in this population. However, they might have overlooked the effect of cancer surveillance bias on these trends. Surveillance bias occurs when a condition is searched with differential intensity across populations or over time, or according to care setting and patient characteristics.2Haut ER Pronovost PJ Surveillance bias in outcomes reporting.JAMA. 2011; 305: 2462-2463Crossref PubMed Scopus (195) Google Scholar Several types of cancer are highly sensitive to the intensity of screening and clinical detection activities, such as prostate, thyroid, and kidney cancers; these cancers have a substantial reservoir of indolent, subclinical forms and are at high risk of being overdiagnosed.3Welch HG Brawley OW Scrutiny-dependent cancer and self-fulfilling risk factors.Ann Intern Med. 2018; 169: 134-135Crossref PubMed Scopus (0) Google Scholar, 4Chiolero A Santschi V Paccaud F Public health surveillance with electronic medical records: at risk of surveillance bias and overdiagnosis.Eur J Public Health. 2013; 23: 350-351Crossref PubMed Scopus (22) Google Scholar One major consequence is that changes in the incidence of such scrutiny-dependent cancers do not simply reveal the effect of changes in carcinogenic exposures; they also result from changes in the frequency and modality of screening and detection activities.2Haut ER Pronovost PJ Surveillance bias in outcomes reporting.JAMA. 2011; 305: 2462-2463Crossref PubMed Scopus (195) Google Scholar Based on the ecological association between obesity and cancer trends, arguing that obesity would be the cause of the increased incidence of cancer among young adults is highly disputable. Nevertheless, since obesity is associated with a greater frequency of medical examinations,5Bertakis KD Azari R Obesity and the use of health care services.Obes Res. 2005; 13: 372-379Crossref PubMed Scopus (102) Google Scholar the probability of detecting scrutiny-dependent cancers could be higher among obese individuals. If we assume differential secular trends in cancer detection intensity by age, the parallel rise in obesity and several types of cancer among young adults could be merely the result of a surveillance bias. I declare no competing interests. Cancer surveillance, obesity, and potential biasAlthough Hyuna Sung and colleagues1 stressed caution in interpreting their ecological study in The Lancet Public Health (March, 2019), the naive reader—or the media, as was the case2—might conclude that obesity is fuelling the reported disproportionate temporal increases in incidence of obesity-related cancers in young adults. However, there are many arguments against obesity as a causal driver. Full-Text PDF Open AccessEmerging cancer trends among young adults in the USA: analysis of a population-based cancer registryThe risk of developing an obesity-related cancer seems to be increasing in a stepwise manner in successively younger birth cohorts in the USA. Further studies are needed to elucidate exposures responsible for these emerging trends, including excess bodyweight and other risk factors. Full-Text PDF Open Access
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,338 | 0,693 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,009 | 0,011 |
| Études des sciences et des technologies | 0,003 | 0,007 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex 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 ».