Impact of the early phase of the COVID pandemic on cancer treatment delivery and the quality of cancer care: a scoping review and conceptual model
Notice bibliographique
Résumé
BACKGROUND: The disruption of health services due to coronavirus disease (COVID) is expected to dramatically alter cancer care; however, the implications for care quality and outcomes remain poorly understood. OBJECTIVE: We undertook a scoping review to evaluate what is known in the literature about how cancer treatment has been modified as a result of the COVID pandemic in patients receiving treatment for solid tumours, and what domains of quality of care are most impacted. METHODS: Citations were retrieved from MEDLINE and EMBASE (from 1 January 2019 to 28 October 2020), utilizing search terms grouped by the key concept (oncology, treatment, treatment modifications and COVID). Articles were excluded if they dealt exclusively with management of COVID-positive patients, modifications to cancer screening, diagnosis or supportive care or were not in English. Articles reporting on guidelines, consensus statements, recommendations, literature reviews, simulations or predictive models, or opinions in the absence of accompanying information on experience with treatment modifications in practice were excluded. Treatment modifications derived from the literature were stratified by modality (surgery, systemic therapy (ST) and radiotherapy) and thematically grouped. To understand what areas of quality were most impacted, modifications were mapped against the Institute of Medicine's quality domains. Where reported, barriers and facilitators were abstracted and thematically grouped to understand drivers of treatment modifications. Findings were synthesized into a logic model to conceptualize the inter-relationships between different modifications, as well as their downstream impacts on outcomes. RESULTS: In the 87 retained articles, reductions in outpatients visits (26.4%) and delays/deferrals were commonly reported across all treatment modalities (surgery: 50%; ST: 55.8% and radiotherapy: 56.7%), as were reductions in surgical capacity (57.1%), alternate systemic regimens with longer treatment intervals or use of oral agents (19.2%) and the use of hypofractionated radiotherapy regimens (40.0%). Delivery of effective, timely and equitable care was the quality domains found to be the most impacted. The most commonly reported facilitator of maintaining cancer care delivery levels was the shift to virtual models of care (62.1%), while patient-initiated deferrals and cancellations (34.8%), often due to fear of contracting COVID (60.9%), was a commonly reported barrier. CONCLUSIONS: As it will take a considerable amount of time for the cancer system to resume capacity and adjust models of care in response to the pandemic, these treatment delays and modifications will likely be prolonged and will negatively impact the quality of care and patient outcomes.
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 | 0,102 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,008 |
| Bibliométrie | 0,031 | 0,032 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,010 | 0,009 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».