Virtual Dermatology and the COVID-19 Pandemic in a Resource-Limited Country Such as Nepal
Bibliographic record
Abstract
Background The COVID-19 pandemic has caused nationwide lockdown, which led to the disruption of health services. Despite being a rising health care modality in Nepal, virtual dermatology services became an effective tool to provide dermatologic care through web-based consultations throughout the country. Therefore, we assessed the implementation of teledermatology services at our center to provide uninterrupted health services across the country during the pandemic. Objective This study aimed to evaluate the clinicodemographic profile of patients using teledermatology services and patient acceptance of this service. Methods A retrospective, single-center, observational study was carried out. Clinicodemographic data from the patients using teledermatology services were obtained and analyzed. A set of questionnaires regarding patients’ acceptance of teledermatology services were administered to the patients through a survey via telephone calls, and the obtained data were interpreted. Results A total of 122 teleconsultations were carried out within the country. The mean age of patients was 33.48 (SD 17.89) years. Of these 122 patients, 79 (64.8%) were from outside and 43 (35.2%) were from inside the city where the institute is located. The average distance from the institute to the patients’ residence was approximately 144.84 (SD 157.20) km, and the mean travel time was approximately 385.31 (SD 889.52) minutes. In total, 89 patients could be contacted, of whom 81 (91%) found the service easy to use, 75 (84.3%) were able to express their problems in a manner similar to that during direct visits, 49 (55.05 %) thought that the teleconsultation was the same as an in-person visit, 80 (89.9%) were satisfied, and 85 (95.5%) agreed to use teledermatology services in the future. Superficial fungal infection was the most common diagnosis (24.6 %). Newly registered patients were more satisfied than follow-up patients (96.36% vs 79.41%, respectively; P=.01). Conclusions This study highlights the importance of virtual dermatology services to deliver dermatologic care during the pandemic in Nepal. In the future, this program has a promising role in providing health care services to meet the medical needs of patients. Conflicts of Interest None declared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".