078 Patient Satisfaction with Telemedicine Appointments in an Academic Andrology-focused Urology Practice during the COVID-19 Pandemic
Bibliographic record
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic has reshaped the landscape of healthcare delivery in much of the world, with telemedicine becoming the standard mode of outpatient healthcare delivery over a span of just a few weeks. There have been no studies to date examining the feasibility of telemedicine in an Andrology-focused urology setting. To evaluate patient satisfaction with telemedicine appointments as an alternative to in-person appointments at an Andology-focused academic urology practice during the COVID-19 pandemic. Between March and June 2020, all appointments at the Andrology-focused practice of a single academic urologist were conducted by telephone. Consecutive patients were contacted by telephone following their appointment to complete a telephone questionnaire. Baseline demographic information was obtained, and perceptions regarding telephone appointments were assessed using a Likert scale. Ninety-six patients completed the telephone questionnaire. Median age was 58.5 years (interquartile range [IQR] 37.3-62.8 years) with 55/96 (57.3%) of the appointments Andrology-focused. Mean distance of residence from the hospital was 8.4 km (IQR 4.7-25.2 km). Only 9/96 (9.3%) of the patients felt that the telephone format did not adequately address their needs. However, 26/96 (27.1) patients said they would prefer an in-person appointment. On multivariable analysis adjusting for age, gender, presenting complaint, type of appointment, education level, and employment status, no factors were associated with feeling that the telephone appointment adequately addressed needs or preference for an in-person appointment in the future. Patients were generally satisfied with telephone appointments as an alternative to in-person appointments during the COVID-19 pandemic. Nonetheless, a substantial portion of patients said they would prefer in-person appointments in the future. Work supported by industry: no.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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".