Preference for breast cancer risk reduction hormonal therapy in women age 50-69 years attending screening mammography.
Notice bibliographique
Résumé
Abstract Abstract #3100 Background: It is expected that tamoxifen, raloxifene and aromatase inhibitors (AIs) will become available for the indication of breast cancer prevention in postmenopausal women. It is unknown how women weigh the benefits and disadvantages of these drugs. The aim of this study was to elicit preferences, using a discrete choice experiment (DCE), for attributes of breast cancer risk reduction hormonal therapies amongst women age 50 – 69 years who attend screening mammography.
 Methods: 500 potentially eligible women were randomly sampled from a screening mammography database (SMD). Women permitting contact from the investigators were invited to participate. Demographic and breast cancer risk factor data was collected from the SMD for participants and non-participants, and compared. The DCE was administered via a postal survey. The attributes and levels for the DCE are outlined in Table 1. A fractional factorial design and matched fold-over technique yielded 16 choice sets. Two dominant choice sets were added. For each choice set, respondents were asked to mark preference for Drug A, Drug B or Neither. The DCE was analyzed using conditional logistic regression (CLR). Regression post estimation calculations (RPECs) were used to predict chemoprevention uptake.
 
 Results: 111 women agreed to be contacted by the investigators, 94 agreed to be sent a survey and 79 returned a completed questionnaire. The proportions of participants who were nulliparous or had a predicted 5-year breast cancer risk >1.66% were significantly higher than for non-participants (p<0.05). No differences were found for age, education or mammographic breast density. From the CLR, all attributes, except decreased risk bone fracture, had coefficients that were significantly different from zero (p<0.01) and of the expected sign. RPECs suggest that the uptake of tamoxifen, raloxifene and AIs would be 13%, 50% and 23% respectively, and that 14% would not consider chemoprevention.
 
 Conclusions: The most important attribute in women's selection of a prevention drug was breast cancer risk reduction. However, the following attributes were still important negative influences on choice: increased risk bone fracture > endometrial cancer > venous thromboembolic event >> cost. Raloxifene may be favoured if all three agents were available. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 3100.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,000 | 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 ».