Evaluation of Patient-Initiated Direct Care Mobile Phone–Based Teledermatology During The COVID-19 Pandemic
Bibliographic record
Abstract
Background With advances in telecommunication, especially smartphones, teledermatology services offered by specialists are now being directly requested by the patients themselves. This model is known as patient-initiated, direct care teledermatology. It has been pushed to the forefront due to the COVID-19 pandemic. Objective The objectives of this study were to determine patients’ satisfaction and dermatologists’ confidence when a diagnosis was made via direct care mobile phone–based teledermatology. Methods Patients availing direct care teledermatology services during the COVID-19 pandemic at a tertiary care center were subjected to a questionnaire within 5 days of the teleconsultation to assess patient satisfaction and opinions regarding using this model during and beyond the current COVID-19 pandemic. The dermatologists rated their confidence in making the clinical diagnosis on a scale from 1-10 for every case. Results Of 437 participants, 419 (95.9%) were satisfied with this mode of teledermatology. An overwhelming majority (n=428, 97.9%) felt safe consulting the dermatologist via teleconsultation and not having to visit the hospital during the COVID-19 pandemic. In addition, 269 (61.6%) patients agreed that they would be happy to use a teledermatology service beyond the COVID-19 pandemic. The dermatologists’ confidence score in making an accurate diagnosis ranged from 3 to 10, with a mean of 9.20 (SD 1.12). Conclusions The high levels of patient satisfaction and dermatologists’ confidence scores indicate that direct care mobile phone–based teledermatology may be a useful tool in providing dermatological services in appropriate settings and its use should continue to be explored beyond the COVID-19 pandemic. Conflicts of Interest None declared.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".