How Effective Is Video Consultation in Clinical Oncology? A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Video-consultation (VC) is a specialized type of telemedicine that uses technology to provide real-time visual and audio patient assessment at a distance. In the present review, we set out to evaluate whether vc is feasible for the assessment, monitoring, and management of oncology patients. METHODS: A search strategy designed to capture studies that addressed the use of telemedicine to deliver cancer care identified relevant articles in the medline (1966 to September 2008) and PubMed (to 2008) databases. Articles were included if they described studies incorporating video-conferencing between patient and provider for assessment or monitoring,physicians or nurses as the care providers,cancer patients,consultation in real-time, and reporting of 1 or more outcomes. RESULTS: Of the more than three hundred articles retrieved, nineteen articles describing 15 unique patient populations involving 709 patients were inclusded in the analysis. No randomized trials were located. Eight studies included a control group; seven involved a case series. The most commonly reported outcomes were patient satisfaction (ten studies), cost to perform consultation (six studies), patient preference for vc compared with in-person consultation (five studies), provider satisfaction (four studies), and provider convenience (four studies). Of these outcomes, satisfaction on the part of patients and physicians has been positive overall, total costs were comparable to or less than those for in-person consultations, and patients valued having vc as an option for consultation. Outcomes evaluating the effect on clinical care were infrequently reported. CONCLUSIONS: While there is evidence to suggest that vc is both feasible and effective for use in the clinical care of oncology patients, studies are generally small and methodologically weak, with limited power of inference.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".