Rapid Expansion of a Teledermatology Web Application for Digital Dermatology Assessment Necessitated by the COVID-19 Pandemic: Retrospective Evaluation
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic necessitated a change in the provision of outpatient care in dermatology. OBJECTIVE: A novel, asynchronous, digital consultation platform was codeveloped with 2 National Health Service dermatology teams to improve access and enhance choice in outpatient care. METHODS: The rollout of the platform was accelerated during the initial COVID-19 lockdown, and its wider use across 2 Scottish health boards was retrospectively evaluated. Integrated with the hospital booking system and electronic patient record, the platform provides an alternative to face-to-face consultations, using information and images submitted by the patients. RESULTS: In total, 297 new patient consultations and 108 return patient consultations were assessed, and 80% (324/405) of the images submitted were of satisfactory quality. The consultations were, on average, 3 minutes shorter than equivalent face-to-face interactions, and a total of 5758 km of patient travel was avoided. Outcomes included web-based reviews (66/405, 16.3%), face-to-face reviews (190/405, 46.9%), biopsies (46/405, 11.4%), discharge (89/405, 22%), and other treatments or investigations (14/405, 3.5%). High levels of patient satisfaction (92/112, 82.1%) were reported. CONCLUSIONS: Digital dermatology assessments are now included in the choices for consultation types that are available to patients, helping to augment service capacity during pandemic recovery.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".