Abstract A19: [Advocate Abstract:] Survivors Teaching Students®: Educating Medical and other Health Professional Students about Ovarian Cancer
Notice bibliographique
Résumé
Abstract “The goal of Survivors Teaching Students (STS) is for future physicians, nurse practitioners, nurses and physician assistants to be able to diagnose ovarian cancer when it is in its earlier, most treatable stages. Survivors Teaching Students is offered in 96 medical schools, 103 nursing schools, 13 Nurse Practitioner schools, 26 Physician Assistant schools, and 12 allied health programs across the United States. Active programs exist in 34 states, the District of Columbia, Virgin Islands, the United Kingdom and Canada. In 2015, the program educated 10,750 students, a 10% increase over the previous year. As of June, 2016, STS volunteers have already presented to approximately 6500 students in 35 states. More than 790 specially trained ovarian cancer survivors volunteer for this program. Each presentation includes a pre- and post-test to assess the student's knowledge about ovarian cancer detection, diagnostic procedures, symptoms, and risk factors, as well as the importance of referral to a gynecologic oncologist. Medical students' and nursing students' knowledge has increased by approximately 23% and 41% respectively during the presentations. Important facts about ovarian cancer: • Ovarian cancer is the most lethal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. • The majority of women are diagnosed when their ovarian cancer is in an advanced stage. • Currently, there is no reliable screening test for the early detection of ovarian cancer. • When detected in an early stage, the survival rates for ovarian cancer greatly improve. • Factors associated with an increased risk of ovarian cancer include a personal or family history of breast, colon, uterine or ovarian cancer, increasing age, never having been pregnant. • Factors associated with a decreased risk of ovarian cancer include using oral contraceptives, having and breastfeeding children, and having a tubal ligation or salpingectomy, hysterectomy or prophylactic removal of the ovaries. Ovarian cancer, even in its early stages, has symptoms: • Bloating • Pelvic or abdominal pain • Difficulty eating or feeling full quickly • Urinary symptoms (urgency or frequency) (Source: Ovarian Cancer Symptoms Consensus Statement - http://www.ocnapartners.org/wp-content/uploads/2013/01/Consensus.pdf) Women who have these symptoms more than 12 times during the course of one month should see a doctor, preferably a gynecologist-especially if the symptoms are new or unusual. Other symptoms have been commonly reported by women with ovarian cancer, including fatigue, indigestion, back pain, pain with intercourse, constipation and menstrual irregularities. However, these symptoms are not as useful in identifying ovarian cancer because they are found just as often in women who do not have the disease. If the symptoms suggest ovarian cancer, three tests should be performed: a complete pelvic exam, including a rectovaginal examination; a transvaginal ultrasound; and a CA-125 blood test. If ovarian cancer is suspected, the woman must be referred to a gynecologic oncologist. The focus of my work as an advocate and regional coordinator for STS includes survivor recruitment and training, outreach and collaboration to medical schools, and leadership of STS facilitators across the southeast United States. Locally in Charlotte, NC, I serve as facilitator and coordinator of Survivors Teaching Students through my role as Community Programs Manager for Lydia's Legacy, a 501(c)(3) non-profit whose mission is to raise awareness of gynecologic cancers through education and fund gynecologic cancer research through donation. Citation Format: Sarah Noonan. [Advocate Abstract:] Survivors Teaching Students®: Educating Medical and other Health Professional Students about Ovarian Cancer. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr A19.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,452 | 0,171 |
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 ».