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Enregistrement W2749509192 · doi:10.7326/m17-0859

Reanalysis of All-Cause Mortality in the U.S. Preventive Services Task Force 2016 Evidence Report on Colorectal Cancer Screening

2017· review· en· W2749509192 sur OpenAlexaboutno aff
Andrew W. Swartz, Jan M. Eberth, Michele J. Josey, Scott M. Strayer

Notice bibliographique

RevueAnnals of Internal Medicine · 2017
Typereview
Langueen
DomaineMedicine
ThématiqueColorectal Cancer Screening and Detection
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute of General Medical Sciences
Mots-clésMedicineTask forceColorectal cancerSouth carolinaSigmoidoscopyDemographyFamily medicineCancerGerontologyInternal medicineColonoscopy

Résumé

récupéré en direct d'OpenAlex

Letters17 October 2017Reanalysis of All-Cause Mortality in the U.S. Preventive Services Task Force 2016 Evidence Report on Colorectal Cancer ScreeningAndrew W. Swartz, MD, Jan M. Eberth, PhD, Michele J. Josey, MS, and Scott M. Strayer, MDAndrew W. Swartz, MDFrom Yukon-Kuskokwim Delta Regional Hospital, Bethel, Alaska, and University of South Carolina, Columbia, South Carolina., Jan M. Eberth, PhDFrom Yukon-Kuskokwim Delta Regional Hospital, Bethel, Alaska, and University of South Carolina, Columbia, South Carolina., Michele J. Josey, MSFrom Yukon-Kuskokwim Delta Regional Hospital, Bethel, Alaska, and University of South Carolina, Columbia, South Carolina., and Scott M. Strayer, MDFrom Yukon-Kuskokwim Delta Regional Hospital, Bethel, Alaska, and University of South Carolina, Columbia, South Carolina.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M17-0859 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Background: The 2016 U.S. Preventive Services Task Force (USPSTF) evidence report on colorectal cancer screening concluded that no colorectal cancer screening methods reduce all-cause mortality. This conclusion was partially based on a meta-analysis of 4 randomized trials that compared flexible sigmoidoscopy screening with no screening. The meta-analysis aggregated results from the 2 age cohorts of 1 of the trials—the NORCCAP (Norwegian Colorectal Cancer Prevention) study—as if these cohorts were a single trial (1). Aggregation of outcomes that have markedly different event rates, screening–control ratios, or both can create a Simpson paradox, a phenomenon where a finding exists in individual data ...References1. Lin JS, Piper MA, Perdue LA, Rutter CM, Webber EM, O'Connor E, et al. Screening for colorectal cancer: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2016;315:2576-94. [PMID: 27305422] doi:10.1001/jama.2016.3332 CrossrefMedlineGoogle Scholar2. Julious SA, Mullee MA. Confounding and Simpson's paradox. BMJ. 1994;309:1480-1. [PMID: 7804052] CrossrefMedlineGoogle Scholar3. Bretthauer M, Gondal G, Larsen K, Carlsen E, Eide TJ, Grotmol T, et al. Design, organization and management of a controlled population screening study for detection of colorectal neoplasia: attendance rates in the NORCCAP study (Norwegian Colorectal Cancer Prevention). Scand J Gastroenterol. 2002;37:568-73. [PMID: 12059059] CrossrefMedlineGoogle Scholar4. Holme Ø, Loberg M, Kalager M. Colorectal cancer and the effect of flexible sigmoidoscopy screening—reply [Letter]. JAMA. 2014;312:2411-2. [PMID: 25490338] doi:10.1001/jama.2014.14695 CrossrefMedlineGoogle Scholar5. R Development Core Team.. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2010. Google Scholar Author, Article, and Disclosure InformationAffiliations: From Yukon-Kuskokwim Delta Regional Hospital, Bethel, Alaska, and University of South Carolina, Columbia, South Carolina.Disclaimer: No entity other than the authors had any input or role in the design, execution, composition, or decision to publish.Acknowledgment: The authors thank Olga Y. Gorlova, PhD (Geisel School of Medicine, Dartmouth University), and Gloria Ann Evans, FNP, DrPH (retired, Durham, North Carolina), for critical manuscript review and stylistic suggestions and Karen Matthias (Anchorage, Alaska) for stylistic suggestions.Financial Support: Dr. Eberth is supported in part by a Mentored Research Scholar Grant from the American Cancer Society (MRSG-15-148-01-CPHPS). Ms. Josey is supported in part by grant T32-GM081740 from the National Institutes of Health, National Institute of General Medical Sciences.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M17-0859.This article was published at Annals.org on 22 August 2017. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byColorectal cancer screening at a younger age: pitfalls in the model-based recommendation of the USPSTFLong-Term Follow-up of the Italian Flexible Sigmoidoscopy Screening TrialCarlo Senore, MD, MSc, Emilia Riggi, PhD, Paola Armaroli, MD, MSc, Luigina Bonelli, MD, Stefania Sciallero, MD, MPhil, Marco Zappa, MD, Arrigo Arrigoni, MD, Claudia Casella, PhD, Cristiano Crosta, MD, Fabio Falcini, MD, Franco Ferrero, MD, Mario Fracchia, MD, Orietta Giuliani, PhD, Mauro Risio, MD, Antonio G. Russo, MD, MPH, Carmen Beatriz Visioli, MD, MSc, Stefano Rosso, MD, MSc, and Nereo Segnan, MD, MPH, for the SCORE Working GroupScreening for Colorectal Cancer Leading into a New Decade: The "Roaring '20s" for Epigenetic Biomarkers?Multicancer Screening: One Size Does Not Fit AllCost-Effectiveness of Colorectal Cancer Genetic TestingParticipatory simulation modeling to inform colorectal cancer screening in a complex remote northern health system: Canada's Northwest TerritoriesEnhancing bowel screening: Preventing colorectal cancer by flexible sigmoidoscopy in New ZealandClinical and Economic Impact of Tailoring Screening to Predicted Colorectal Cancer Risk: A Decision Analytic Modeling StudyStrategies for Colorectal Cancer ScreeningAll‐cause mortality versus cancer‐specific mortality as outcome in cancer screening trials: A review and modeling studyEstimating the Effect of Preventive Services With Databases of Administrative Claims: Reasons to Be ConcernedOverall mortality in men and women in the randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening TrialYou Should Get Screened for Colon Cancer, ReallyWhy What You May Not Know About Fecal Immunochemical Testing MattersAndrew W. Swartz, MDHow I do it: Does this cost-effectiveness analysis convince me about screening for Barrett's esophagus?Monitoring the performance of sigmoidoscopy screening: the need for a comprehensive approachPreventing colorectal cancer or early diagnosis: Which is best? A re-analysis of the U.S. Preventive Services Task Force Evidence ReportFindings during screening colonoscopies in a Middle Eastern cohortOccult blood in faeces: a window into health beyond the colorectum?Long-Term Effectiveness of Sigmoidoscopy Screening on Colorectal Cancer Incidence and Mortality in Women and Men A Randomized TrialØyvind Holme, MD, PhD, Magnus Løberg, MD, PhD, Mette Kalager, MD, PhD, Michael Bretthauer, MD, PhD, Miguel A. Hernán, MD, DrPH, Eline Aas, PhD, Tor J. Eide, MD, PhD, Eva Skovlund, MSc, PhD, Jon Lekven, MD, PhD, Jörn Schneede, MD, PhD, Kjell Magne Tveit, MD, PhD, Morten Vatn, MD, PhD, Giske Ursin, MD, PhD, and Geir Hoff, MD, PhD, for the NORCCAP Study Group†† 17 October 2017Volume 167, Issue 8Page: 602-603KeywordsCancer preventionCancer screeningCohort studiesColorectal cancerColorectal cancer screeningFlexible sigmoidoscopyLung and intrathoracic tumorsMortalityOvarian cancerRelative risk ePublished: 22 August 2017 Issue Published: 17 October 2017 Copyright & PermissionsCopyright © 2017 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,960
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,259
Tête enseignante GPT0,495
Écart entre enseignants0,236 · 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 tête enseignante, pas un consensus.

Devis d'étudeAutre devis
Domainenon disponible
GenreSynthèse

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

En bref

Citations32
Publié2017
Routes d'admission1
Résumé présentoui

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