Surveying the Challenges to Improve Linear Accelerator-based Radiation Therapy in Africa: a Unique Collaborative Platform of All 28 African Countries Offering Such Treatment
Notice bibliographique
Résumé
Radiation therapy is a critical component for curative and palliative treatment of cancer and is used in more than half of all patients with cancer. Yet there is a global shortage of access to this treatment, especially in Sub-Saharan Africa, where there is a shortage of technical staff as well as equipment. Linear accelerators (LINACs) offer state-of-the-art treatment, but this technology is expensive to acquire, operate and service, especially for low- and middle-income countries (LMICs), and often their harsh environment negatively affects the performance of LINACs, causing downtime. A global initiative was launched in 2016 to address the technology and system barriers to providing radiation therapy in LMICs through the development of a novel LINAC-based radiation therapy system designed for their challenging environments. As the LINAC prototype design phase progressed, it was recognised that additional information was needed from LMICs on the performance of LINAC components, on variables that may influence machine performance and their association, if any, with equipment downtime. Thus, a survey was developed to collect these data from all countries in Africa that have LINAC-based radiation therapy facilities. In order to understand the extent to which these performance factors are the same or different in high-income countries, facilities in Canada, Switzerland, the UK and the USA were invited to participate in the survey, as was Jordan, a middle-income country. Throughout this process, LMIC representatives have provided input on technology challenges in their respective countries. This report presents the method used to conduct this multilevel study of the macro- and microenvironments, the organisation of departments, the technology, the training and the service models that will provide input into the design of a LINAC prototype for a LINAC-based radiation therapy system that will improve access to radiation therapy and thus improve cancer treatment outcomes. It is important to note that new technology should be introduced in a contextual manner so as not to disrupt existing health systems inadvertently, especially with regards to existing staffing, infrastructure and socioeconomic issues. A detailed analysis of data is underway and will be presented in a follow-up report. Selected preliminary results of the study are the observation that LINAC-based facilities in LMICs experience downtime associated with failures in multileaf collimators and vacuum pumps, as well as power instability. Also, that there is a strong association of gross national product per capita with the number of LINACs per population.
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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 | 0,002 | 0,001 |
| 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) | 0,000 | 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 ».