Evaluating the Quality of Asynchronous Versus Synchronous Virtual Care in Patients With Erectile Dysfunction: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Asynchronous health care encounters are becoming an increasingly mainstream form of telehealth. While synchronous phone or video visits have become more widely accepted, US policymakers and other key health care stakeholders have been hesitant to fully embrace asynchronous diagnosis and treatment. This is particularly true in the context of direct-to-consumer (DTC) platforms, where encounters are patient-initiated and there is no preestablished relationship with a provider. This hesitation is compounded by limited research comparing outcomes between asynchronous and synchronous care, especially in the DTC context. OBJECTIVE: The purpose of this study was to explore whether asynchronous care leads to different patient outcomes in the form of medication-related adverse events when compared to synchronous virtual care. METHODS: Using 10,000 randomly sampled patient records from a prominent US-based DTC platform, we analyzed the rates of patient-reported side effects from commonly prescribed erectile dysfunction medications and compared these rates across modalities of treatment. RESULTS: Asynchronous care resulted in lower but nonsignificant differences in the rates of the reported drug-related side effects compared to synchronous treatment. CONCLUSIONS: In some circumstances, such as treatment for erectile dysfunction, asynchronous care can offer the same level of safety in prescribing when compared to synchronous care. More research is needed to evaluate the safety of asynchronous care across a wider set of conditions and measures.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".