The role of molecular, clinical and socioeconomic factors in the long-term survival of axillary node negative breast cancer patients
Notice bibliographique
Résumé
The overall objective of this study was to investigate factors associated with \nlong-term survival in axillary node negative (ANN) breast cancer patients. Clinical \nand biological factors included stage, histopathologic grade, p53 mutation, Her-2/neu \namplification, estrogen receptor status (ER), progesterone receptor status (PR) and \nvascular invasion. Census derived socioeconomic (SES) indicators included median \nindividual and household income, proportions of university educated individuals, \nhousing type, "incidence" of low income and an indicator of living in an affluent \nneighbourhood. The effects of these measures on breast cancer-specific survival and \ncompeting cause survival were investigated. \nA cohort study examining survival among axillary node negative (ANN) breast \ncancer patients in the greater Toronto area commenced in 1 989. Patients were \nfollowed up until death, lost-to-follow up or study termination in 2004. Data were \ncollected from several sources measuring patient demographics, clinical factors, \ntreatment, recurrence of disease and survival. Census level SES data were collected using census geo-coding of patient addresses' at the time of diagnosis. Additional \nsurvival data were acquired from the Ontario Cancer Registry to enhance and extend \nthe observation period of the study. Survival patterns were examined using KaplanMeier \nand life table procedures. Associations were examined using log-rank and \nWilcoxon tests of univariate significance. Multivariate survival analyses were \nperfonned using Cox proportional hazards models. Analyses were stratified into less \nthan and greater than 5 year survival periods to observe whether known markers of \nshort-tenn survival were also associated with reductions in long-tenn survival among \nbreast cancer patients. \nThe 15 year survival probabilities in this cohort were: for breast cancerspecific \nsurvival 0.88, competing causes survival 0.89 and for overall survival 0.78. \nEstrogen receptor (ER) and progesterone receptor (PR) status (Hazard Ratio (HR) ERIPR- \nversus ER+/PR+, 8.15,95% CI, 4.74, 14.00), p53 mutation (HR, 3.88, 95% CI, \n2.00, 7.53) and Her-2 amplification (HR, 2.66, 95% CI, 1.36, 5.19) were associated \nwith significant reductions in short-tenn breast cancer-specific survival «5 years \nfollowing diagnosis), however, not with long-term survival in univariate analyses. \nStage, histopathologic grade and ERiPR status were the clinicallbiologieal factors that \nwere associated with short-term breast cancer specific survival in multivariate results. \nLiving in an affluent neighbourhood (top quintile of median household income \ncompared to the rest of the population) was associated with the largest significant \nincrease in long-tenn breast cancer-specific survival after adjustment for stage, \nhistopathologic grade and treatment (HR, 0.36, 95% CI, 0.12, 0.89).
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 |
| 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 ».