Integrated Model of Cancer Control for Early Detection and Treatment in Adolescents and Young Adults Living With HIV: Protocol for a Cluster Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Zambia has one of the highest prevalence rates of HIV among adolescents and young adults (AYA) living with HIV in sub-Saharan Africa, which accounts for half of all new HIV cases as of 2023. Compared to their peers who are not living with HIV, AYA living with HIV are more likely to develop cancer. The most frequently diagnosed cancers among AYA living with HIV in Zambia are cervical cancer, Kaposi sarcoma, and non-Hodgkin lymphoma. Premature cancer mortality among AYA living with HIV is driven by late-stage presentation and poor treatment adherence. OBJECTIVE: We aim to develop and test an integrated model of cancer control for AYA living with HIV that can be delivered as an embedded component in existing HIV treatment programs in primary care facilities and linked with specialized treatment at cancer centers. METHODS: We propose a cluster randomized controlled trial to compare the AYAHIV Role-Based Responsibilities for Oncology-Focused Workforce (ARROW) program with a one-time education campaign. The ARROW program consists of interventions at the individual, health care provider, and health system levels. Peer counselors will educate AYA living with HIV through one-on-one and group education sessions and offer care coordination and linkages with clinicians. The HIV and oncology workforce will receive collaborative education and training. The ARROW Health Care Collaborative will connect administrators and policy makers to address system-level barriers. The study will recruit AYA living with HIV between the ages of 15 and 39 years who have been on antiretroviral therapy for at least 6 months and are not pregnant; the cancer treatment cohort will enroll AYA living with HIV who have been diagnosed with cervical cancer, Kaposi sarcoma, or non-Hodgkin lymphoma in Lusaka, Zambia. Half of the 18 HIV facilities have been randomly assigned to a one-time educational campaign and the other half to the ARROW intervention. Participants in the cancer treatment cohort will be randomized into 1 of the 2 study arms. We will conduct economic evaluations to assess the cost-effectiveness of the ARROW program. We will use an intent-to-treat approach to test the hypothesis that AYA living with HIV in the ARROW program will have higher uptake of diagnostic services, increased adherence to treatment, and improved outcomes compared to those receiving one-time education. RESULTS: As of March 2025, early detection cohort recruitment concluded with 3442 participants and cancer treatment cohort enrollment is ongoing, with 105 participants thus far. Results pertaining to the 12-month end points will be available in early 2026. CONCLUSIONS: If successful, the ARROW program will offer a model to improve cancer prevention, early diagnosis, and treatment through improved integration between HIV and cancer services. Furthermore, ARROW can provide a framework for implementing expanded services, such as survivorship care, for AYA living with HIV. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68254.
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 | 0,040 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,003 |
| Méta-épidémiologie (sens large) | 0,013 | 0,010 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,007 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,089 | 0,009 |
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 ».