The Impact of Virtual Cancer Care on Chemotherapy Delivery and Clinical Outcomes in Colorectal Cancer Patients Receiving Systemic Therapy: A Pre- and Intra-Pandemic Analysis
Bibliographic record
Abstract
(1) Background: The coronavirus 2019 pandemic has resulted in an abrupt transition to virtual oncology care worldwide. This study’s objective is to evaluate chemotherapy delivery and clinical outcomes in patients on systemic treatment for colorectal cancer before and during the pandemic. (2) Methods: Clinical data was collected on patients with colorectal cancer receiving intravenous chemotherapy at The Ottawa Hospital from June 2019 to March 2021. Patients were stratified by whether they were started on chemotherapy pre-pandemic (June 2019–January 2020) or intra-pandemic (February 2020–March 2021). Multiple regression analysis was used to compare outcomes between pandemic periods; (3) Results: There were 220 patients included in this study. The proportion of virtual consultations (1.2% to 64.4%) and follow-up visits (5.2% to 83.3%) increased during the pandemic. There was no difference in the incidence of treatment delays (OR = 1.01, p = 0.78), chemotherapy dose reductions (OR = 0.99, p = 0.69), emergency department visits (OR = 1.23, p = 0.37) or hospitalizations (OR = 0.73, p = 0.43) between pandemic periods. A subgroup analysis revealed no difference in outcomes independent of the presence of metastases; (4) Conclusion: These findings serve as an important quality-care indicator and demonstrate that virtual oncology care appears safe in a cohort of high-risk colorectal cancer patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".