Virtual Care in Patients with Cancer: A Systematic Review
Bibliographic record
Abstract
Virtual care in cancer care existed in a limited fashion globally before the COVID-19 pandemic, mostly driven by geographic constraints. The pandemic has required dramatic shifts in health care delivery, including cancer care. We conducted a systematic review of comparative studies evaluating virtual versus in-person care in patients with cancer. Embase, APA PsycInfo, Ovid MEDLINE, and the Cochrane Library were searched for literature from January 2015 to 6 August 2020. We adhered to PRISMA guidelines and used the modified GRADE approach to evaluate the data. We included 34 full-text publications of 10 randomized controlled trials, 13 non-randomized comparative studies, and 5 ongoing randomized controlled trials. Evidence was divided into studies that provide psychosocial or genetic counselling and those that provide or assess medical and supportive care. The limited data in this review support that in the general field of psychological counselling, virtual or remote counselling can be equivalent to in-person counselling. In the area of genetic counselling, telephone counselling was more convenient and noninferior to usual care for all outcomes (knowledge, decision conflict, cancer distress, perceived stress, genetic counseling satisfaction). There are few data for clinical outcomes and supportive care. Future research should assess the role of virtual care in these areas. Protocol registration: PROSPERO CRD42020202871.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".