Teledermatology During the COVID-19 Pandemic in a Developing Country: Could This Be the Answer to Improving the Reach of Dermatology Care?
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has changed the way telemedicine is utilized globally, with most countries reporting an increase in its adoption. The first review of teledermatology in Nigeria reported poor service use. This present report outlines an improvement in the adoption of telemedicine and factors that may account for this increased utilization. Methods: We performed a retrospective review of all patients who registered through 2 telemedicine platforms to access dermatology services during a 1-year period. Data were collated with an online questionnaire and analyzed by t test and chi-square test. Results: Sixty patients booked appointments through the platform, with 38 and 22 patients booking virtual and physical consultations, respectively. The response rate was 68.4% and 50.0% for those that booked virtual and physical consultations, respectively, with no significant age difference between the groups ( P = 0.42). More women booked appointments, and all respondents were educated to at least college level. The majority learned about booking a dermatologist appointment through social media. There was no difference in the clinical outcome between patients who booked a virtual or physical consultation ( P = 0.89). Conclusion: More patients are resorting to teledermatology services because of the convenience; however, most of these patients are situated in urban areas. Because this study showed no significant differences in the clinical outcome between patients who booked virtual and physical consultations, the adoption of teledermatology may be the answer to increasing coverage of dermatology services in developing countries where specialists are limited. Efforts must be made to increase the reach of teledermatology to rural areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".