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Enregistrement W4414218211 · doi:10.1136/ebm-2025-pod.44

044 Breast cancer screening: using epidemiological misinformation to push an agenda – ‘biased-evidence’ medicine

2025· article· en· W4414218211 sur OpenAlexaffabout
Donna L. Reynolds, James A. Dickinson, Guylène Thériault, Roland Grad

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Cancer Incidence and Screening
Établissements canadiensUniversité de MontréalUniversity of CalgaryMcGill UniversityCARE CanadaUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésObservational studyOverdiagnosisBreast cancerMisinformationEpidemiologyObservational methods in psychologyBreast cancer screeningAlternative medicineIncidence (geometry)

Résumé

récupéré en direct d'OpenAlex

Lies, damned lies and statistics! (Mark Twain and/or British Prime Minister Benjamin Disraeli). In Canada, breast cancer screening has become polarized with longstanding advocates pushing for more frequent testing, and extending the ages to start or stop. From 2023 to 2025, the Canadian Task Force on Preventive Health Care (CTFPHC) updated its 2018 guideline on screening for breast cancer. At the same time, an organized multi-pronged pro-screening campaign exerted pressure to expand screening. Proponents used several observational studies of various designs and statistics to advance the legitimacy of their narrative. Either willfully or uneducatedly, this information was used to justify biased and erroneous assertions in advocacy campaigns and other communications. Errors in simple statistical concepts such as relative vs absolute risk reduction, survival vs mortality, ‘peak age’ vs age-specific or age-adjusted rates and others were used to conflate data estimates to buttress a pro-screening agenda. We will present examples of how screening advocates selectively used epidemiological data and observational studies to promote their agenda. This includes: selective interpretation of the minimal rise in incidence of cancer in younger age groups; differing incidence and mortality rates for various ethnic/racial groups; inappropriate and unorthodox statistical metrics (relative risk, peak age, proportions); shift from randomized controlled trials to observational studies; morbidity over mortality; survival versus mortality, and overinterpreting modeling data. Harms of screening were minimized by rarely providing numerical estimates and framing test-related anxiety as transient. Investigation of positive tests that were not cancer (i.e., false positives) and overdiagnosis were acknowledged as concepts, but spoken about in generalities. These issues were omitted from the narrative of expanding to more frequent screening, to lower and raise screening age groups and effects of increasing comorbidities. The long-term impact of the misinformation campaign remains unclear, but the Minister of Health requested an external review of the CTFPHC. A second consequence has been the fear of ‘blow-back’ from screening advocates among supporters of the Task Force and its evidence-based recommendations. When evidence is erroneous, misused or misrepresented, consequences can befall a trusted organization. What was experienced by the CTFPHC could occur to any evidence-based group. We share lessons we learned with the hope of alerting and preparing groups on the methods and approach of ‘biased-evidence’ medicine. Objectives Describe how epidemiological data, observational studies and statistics were misused to advance the legitimacy of a pro-screening agenda during a national task force’s development of breast cancer screening recommendations Discuss the impact of advocacy and pressure groups’ misinformation campaigns and resultant political interference on evidence-based guidelines and recommendations that are contrary to their positions Share insights and cautions from our experience with participants to be better prepared for ‘biased-evidence’ medicine Seminar: From 2023 to 2025, the Canadian Task Force on Preventive Health Care (CTFPHC) updated its 2018 guideline on screening for breast cancer. We will review how statistics, epidemiological data and observational studies were misused by pro-screening advocates to advance the legitimacy of their narrative to expand screening. Their information was used to justify biased and erroneous assertions in advocacy campaigns and other communications. We will engage seminar participants on lessons learned, so as to be alert and prepared for ‘biased-evidence’ medicine. We will also discuss potential approaches to address this. Presenters have been involved in the recent saga. Results The recommendations on breast cancer screening in Canada have been a subject of debate for nigh on 50 years. Much has been written on this history in the book Conspiracy of Hope by Renee Pellerin but the debate is ongoing. We will show how advocacy groups misuse observational studies and statistics to push their agenda, and drive overuse of tests (in this instance screening mammography) with resultant overdiagnosis, and diversion of resources. Conclusions When evidence is erroneous, misused or misrepresented, consequences can befall a trusted organization. We will show how advocacy and pressure groups misuse epidemiological data, observational studies and statistics to further the legitimacy of their agenda. Either willfully or ignorantly, this information is used to justify biased and erroneous assertions in advocacy campaigns and other communications. The result leads to overuse of tests (in this instance screening mammography) and resultant increase in overdiagnosis. We share lessons learned while updating the CTFPHC’s breast cancer screening guideline with the hope of alerting and preparing groups on the subject of ‘biased-evidence’ medicine.

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)0,173
score de la tête « metaresearch » (Gemma)0,318
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,827
Score d'incertitude au seuil0,913

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1730,318
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0060,003
Études des sciences et des technologies0,0050,048
Communication savante0,0180,020
Science ouverte0,0050,010
Intégrité de la recherche0,0200,030
Charge utile insuffisante (le modèle a refusé de juger)0,0080,004

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 Opus0,377
Tête enseignante GPT0,491
Écart entre enseignants0,114 · 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.

Devis d'étudeSans objet
DomaineMéthodes
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

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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