Teledermatology in times of COVID‐19
Bibliographic record
Abstract
Remote consultations are likely to grow in importance in the following years, especially if the coronavirus disease 2019 (COVID-19) pandemic continues. Patients' opinions on teledermatology have already been analyzed, but a current analysis during the COVID-19 pandemic is lacking. The purpose of this survey was to investigate the satisfaction of patients who had received dermatological advice via telephone during the COVID-19 pandemic and to analyze their general opinion about eHealth as well as possible limitations for a broad implementation. Ninety-one patients managed in the dermatology department using telephone consultation during the COVID-19 pandemic were interviewed. An anonymous questionnaire, including the established quality of life questionnaire (Dermatology Life Quality Index [DLQI]), was used. It was found that men were more satisfied with telephone consultations than women (p = 0.029), educational level and age did not correlate with satisfaction (p = 0.186 and 388, respectively), and the longer the waiting time for a telephone consultation, the lower the satisfaction (p = 0.001). Grouped analysis of all participants showed that the majority (54.0% n = 38/71) were "very happy" with the telephone consultation. Higher disease burden (DLQI) was associated with lower satisfaction (p = 0.042). The main stated reasons for using telemedicine were shorter waiting times (51.6% n = 47/91) and no travel requirement (57.1% n = 47/91). Almost one-quarter (23.1% n = 21/89) of patients would use teledermatology in the future, 17.6% (n = 16/89) would not, and 57.1% (n = 51/89) would only use it in addition to a traditional consultation with personal contact. In conclusion, most patients in the study group still preferred traditional face-to-face medical consultations to telephone consultations, but also desired an add-on telemedical tool. Dermatological care using more modern telemedicine technologies than telephone conferencing is needed to better address patients' desires, especially in times of the COVID-19 pandemic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".