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
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.031 | 0.032 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".