Grassroots Partnership to See and Treat Cervical Cancer in Rural Uganda
Notice bibliographique
Résumé
Abstract 9 Background: In Uganda, cervical cancer is the leading cause of cancer death, affecting 45 in every 100,000 women annually and killing 25 in every 100,000 annually. To effect change, two Canadian registered charities partnered with a Ugandan nongovernmental organization, a university, and the Ministry of Health to develop a novel screening, treatment, and educational training program. The two major goals of our program were to develop a training program for health care providers in southwestern Uganda for visual inspection of the cervix with acetic acid (VIA) and a cryotherapy see and treat model; and to implement the first cervical cancer screening program of its kind in the Kabale region of southwestern Uganda. Methods: Our program was developed in partnership with Mbarara University of Science and Technology, a grass-roots Ugandan community development organization (Kigezi Healthcare Foundation [KIHEFO]), a Canadian charity that is focused on providing medical and dental care and educational training and infrastructure development (Bridge to Health Medical and Dental), and a Canadian charity that is focused on treatment for advanced cervical cancer (Road to Care). Results: Requisite supplies were obtained by Bridge to Health Medical and Dental and left behind with KIHEFO. A partnership was formed between academia, government, and civil society across Canada and Uganda. Over 5 days, 15 Ugandan health care workers were trained in VIA and cryotherapy, and 96 patients were screened for cervical cancer. Six patients were successfully treated for precancerous lesions. One biopsy was sent for pathology review and analysis. Conclusion: Since the pilot program, KIHEFO has conducted two additional cervical cancer screening programs using VIA and the see and treat approach. A new cervical cancer screening and treatment campaign, along with a quality control and educational training refresher, for the original 15 health care providers is planned for February 2017. Funding: Bridge to Health Medical and Dental and Kigezi Healthcare Foundation in partnership with the Ugandan Ministry of Health. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST No COIs from the authors.
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 | 0,000 | 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 | 0,000 |
| 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 ».