Prevalence and patterns of self‐initiated nutritional supplementation in men at high risk of prostate cancer
Notice bibliographique
Résumé
Authors from Philadelphia have studied the prevalence and patterns of self‐initiated herbal and vitamin supplementation among men at high risk of developing prostate cancer. They found that many patients at risk take measures to try and reduce this risk, even if the items that they take have not necessarily been shown to be effective. Another group of authors from Chicago evaluated the effect of seminal vesical invasion on survival in prostate cancer, and in particular attempted to validate Kattan's nomogram in this pathological subgroup. The authors from Vancouver attempted to identify sexual information resource preferences of patients before and after definitive treatment for early‐stage prostate cancer with either radical prostatectomy or brachytherapy. They strongly advocate the need for physicians to offer patients access to such information. A group of authors from Brisbane investigated the effects of pharmacological treatments as apposed to clinical monitoring on quality of life in patients with non‐localised prostate cancer, and found that the adverse effects on quality of life are important in deciding the timing of androgen suppression. OBJECTIVE To define the prevalence and patterns of self‐initiated herbal and vitamin supplementation among men at high risk of developing prostate cancer, as there is increasing public awareness of prostate cancer screening, risk‐factor assessment and prevention, leading to increasing interest in the use and systematic study of nutritional therapies for prostate cancer prevention. SUBJECTS AND METHODS Since 1996 our institution has prospectively maintained a prostate cancer‐risk registry through its Prostate Cancer Risk Assessment Program (PRAP). Eligibility includes African‐American men, any man with at least one first‐degree relative or two or more second‐degree relatives with prostate cancer, or men who tested positively for the BRCA1 gene mutation. A 420‐item self‐administered questionnaire was completed and included the use of nutritional supplements and complementary therapies. We divided men into groups who used supplements to lessen their cancer risk and those who did not. The prevalence and patterns of use were evaluated and the two groups then compared for differences in demographic, socio‐economic and risk‐perception variables. RESULTS In all, 345 high‐risk men were enrolled in the PRAP over a 5‐year period. Data on the use of dietary or herbal supplements were available on 333 men (97%), of whom over half (170) reported taking one or more supplements to prevent prostate cancer. Supplement use was divided into eight categories, including vitamins, minerals, extracts from fruits/seeds, organic compounds, flowers/bulbs, leaves/bark, roots, or animal products. Most commonly used for self‐initiated chemoprevention were vitamins (95%), minerals (28%), and fruit/seed extracts (18%). More than a quarter of men (27%) took three or more agents. Men taking proactive preventative measures were statistically more likely to be Caucasian and aged > 60 years ( P < 0.05). African‐Americans were less likely to self‐initiate preventative steps. Men taking supplements tended to return more often for follow‐up and participate in PRAP longer, while those not taking supplements tended to earn less and report less self‐perceived risk. CONCLUSIONS A significant proportion of men at risk of developing prostate cancer initiate measures they perceive to reduce their risk. Although the chemopreventative efficacy of many of these supplements remains unsubstantiated, they are widely perceived by the public to reduce the risk of developing prostate cancer. These data provide an insight into patient perceptions and misconceptions of chemopreventative strategies, and may help to refine recruitment efforts in multi‐institutional prostate cancer prevention trials.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».