Bibliographic record
Abstract
The COVID-19 pandemic precipitated the acute and efficient rollout of telehealth and virtual health care around the world. This review article focuses on the adoption of virtual care in the management of oncology patients, and discusses how virtual care offers the potential for large-scale, positive impacts on access to clinical trials. Virtual care during and following the peak of the pandemic has been found to be both safe and efficacious for oncology patients. Features, such as wearable health technologies, remote monitoring, home visits, and investigations being done closer to home, represent just some of the strengths of the virtual assessment rollout that were successfully utilized. One of the primary criticisms of oncological clinical trials is that clinical trial participants are not always representative of the patient populations treated in routine practice. This is in part due to stringent inclusion criteria and more broadly pertains to a lack of access to clinical trials, many of which are geographic as most trials are conducted in an urban, academic, or 'centralized' center. This paper seeks to discuss the barriers to clinical trial participation and to propose that the virtual care transformation that occurred during the pandemic has equipped oncological clinicians and researchers with the tools to better address these obstacles. A review of the literature on the impact of the virtual care rollout during and after the peak of the COVID-19 pandemic both locally and abroad was conducted. It is proposed that improving patient access through the decentralization of clinical trials has the potential to enhance evidence-based, real-world data, and to produce generalizable trial results that ultimately improve 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 | 0.659 | 0.738 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.012 | 0.018 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".