Implementation of Teledermatology in Alberta, Canada: A Report of One Thousand Cases
Bibliographic record
Abstract
BACKGROUND: Teledermatology utilizes photoimaging and background information to allow dermatologists to remotely provide a diagnosis to practitioners. ConsultDerm is an asynchronous, web-based teledermatology software that allows practitioners to submit their electronic referrals for assessment by board-certified dermatologists. OBJECTIVE: Our study aimed to retrospectively analyze teledermatology's utilization in Canada by using the teledermatology platform ConsultDerm. METHODS: After implementing inclusion criteria, 1000 patients were selected, and relevant demographic and clinical information were extracted for data analysis. In addition, an online survey with pre-formulated questions was distributed to 7 dermatologists currently using the ConsultDerm platform to determine their experience in utilizing teledermatology. RESULTS: Of the 1000 patients, 66.5% had not received treatment prior to their teledermatology referral, and on average, patients experienced symptoms for 489.5 days prior to their referral. Diagnoses made were categorized by conditions, most common being dermatitis (37.1%), followed by acneiform conditions (10.6%), benign lesions/neoplasms (12.1%), infections (9.4%), and dyspigmentation (3.1%). Most consults originated from small population centers, and the referring practitioners were predominantly family physicians. Dermatologists utilizing the platform expressed ease of use, however, areas of improvement were identified such as increasing the quality of imaging and more detailed patient history. CONCLUSION: Through our analysis of 1000 cases, we identified how a teledermatology consultation could be used to assess a wide variety of cutaneous conditions, improving access for patients who may face barriers to seeing a dermatologist.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".