The risks associated with the widespread use of telemedicine in oncology: Four cases and review of the literature
Bibliographic record
Abstract
BACKGROUND: COVID-19 changed the way we practice oncology in multiple ways. Because most cancer patients are comorbid or immunocompromised, we are trying as much as possible to reduce their risk of infection. Marginal just 2 years ago, telemedicine quickly became preeminent with the pandemic to reduce hospital exposure. However, using only virtual visits in oncology patients risk delaying cancer diagnosis or the identification of a complication. CASE SERIES: We present here four cases where a serious medical problem evident on physical exam was overlooked during a virtual visit. Two of our patients experienced a delay in cancer diagnosis thus putting them at risk of local or distant spread. The two others were established oncology patients where a serious medical complication was missed on a virtual visit. CONCLUSIONS: Now more than a year into the pandemic, telemedicine has clearly been a useful tool by limiting unnecessary hospital visits. Yet, as our cases illustrate, its use in oncology without clear boundary can undermine the quality of care. Now that effective vaccines are reducing the transmission and the severity of infection, most oncology patients can be evaluated by a real-time visit.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".