RE: Pan-Canadian Study of Mammography Screening and Mortality From Breast Cancer
Notice bibliographique
Résumé
We were struck by the large reduction in breast cancer mortality reported by Coldman et al. in association with participation in Canadian mammography screening programs ( 1 ), in particular with the consistency in the sizes of the observed reductions across all provinces and across all age groups. Overall, they reported a 40% reduction in breast cancer mortality associated with ever participating in a provincial breast screening program. We conducted a cohort-based analysis of women enrolled in the Canadian National Breast Screening study (CNBSS) and found that screening initiated before age 50 years was not associated with a decline in mortality before age 60 years ( 2 ). In our study, the hazard ratio for death from breast cancer associated with entry into the screening program was 1.10 (95% confidence interval [CI] = 0.86 to 1.40). The screening period in our study was earlier (1980 to 1985) than in the Coldman study (1990 to 2009) and the age groups were different, but most importantly, in the CNBSS, screening was assigned at random, whereas in the Coldman study the screened women were volunteers. The reduction in mortality cannot readily be explained by confounding (‘healthy screening’ bias) because comorbidity and lifestyle do not have sufficient impact on outcome after a diagnosis of breast cancer such that they could explain such a profound mortality difference. Selection bias is a greater concern ( 3 ). We excluded women with a past history of breast cancer from both subcohorts, and we excluded women who had a recent mammogram. If a woman has been diagnosed with breast cancer in the past she will not be a candidate to enroll in a breast cancer screening program. For example, a woman might have been diagnosed in 1989 and die in 1995. In an observational study, her person-years (from 1990 to 1995) and death (in 1995) would be counted among the unscreened women, unless women with prior cancer were specifically excluded. Consider a woman who had breast screening prior to 1990; if she had cancer she would then be counted in the nonscreened cohort, and if she didn’t have cancer she would be eligible to participate in the screening program. In the latter case, her a priori risk of cancer would be reduced, as would her risk of dying of cancer. For this reason, we excluded women who had a recent screen from the CNBSS study from the outset. Of note, in the Pan-Canadian study, 45% of Ontario nonparticipants had a screen outside the program and yet the hazard ratio was still strongly protective (hazard ratio = 0.73, 95% CI = 0.68 to 0.78). We do not know how many women had a mammogram prior to entry. To alleviate our concern that selection bias influenced the results of their study, it would be helpful if Coldman et al. ( 1 ) would provide the dates of diagnosis of breast cancer for the women who died of cancer during the study period. Also, if bias were present, we should see a more extreme protective effect for the first decade (1990–1999) than for the second decade (2000–2009).
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,010 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».