U.S. Preventive Services Task Force (USPSTF) recommendations on breast cancer screening (BCS): Are they justified?
Notice bibliographique
Résumé
1584 Background: Nine randomized population trials (RPTs) on BCS with mammography have been reported. Conclusions based upon mortality (MOR) comparisons in RPTs have long led to uncertainty about whether BCS saves lives, especially in women in their 40s. In Nov 2009, USPSTF recommended against BCS in women 40-49 and biennial BCS for women >50. By 2010, BCS fell by >4% in US. Does the evidence support USPSTF recommendations? Methods: MOR quantifies the effect of intervention across an entire population. However, the key issue is whether BCS reduces MOR among those with disease. This is reflected by survival (SUR), not MOR. While SUR is considered flawed due to conventional screening biases, the confounding influence of these biases is misunderstood. In a RPT, randomization provides an opportunity to eliminate these biases, so that (SUR) may accurately reflect screening efficacy. MOR would be biased if randomization fails to produce populations at equal risk for the target disease. Results: Among 9 RPTs, significant MOR reductions were reported only in Swedish Two-County Study in women >50 and in the Gothenburg Study in women 39-49. However, problems with randomization were responsible for MOR overestimating BCS efficacy in these trials. In 7 RPTs, significant MOR reductions were not seen. However, MOR underestimated BCS efficacy in several RPTs. The Canadian National BCS Study, the most influential RPT in women <50 yrs, showed a trend toward increased BC MOR in the screened group. However, randomization failure was likely responsible for significantly more high risk BC in the screening group, which confounded MOR comparisons. Conclusions: It’s never been possible to justify BCS based on MOR comparisons in RPTs. While meta-analyses have been widely utilized, they can be biased when MOR fails to accurately reflect BCS in individual RPTs. Abundant evidence from RPTs supports that BCS leads to significant stage and SUR advantages not attributable to conventional biases, and which more accurately reflect BCS efficacy. The magnitude of benefit is similar for women in their 40s and for those >50. USPSTF recommendations are not supported by the data. If fully implemented, these guidelines can lead to increased BC MOR in US.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 tête enseignante, 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 ».