Bibliographic record
Abstract
Background The COVID-19 pandemic has accelerated the development and widespread adoption of teledermatology both locally and globally. As dermatology is predominantly a visual specialty, teledermatology is particularly useful for patient care and collaboration between health care professionals. Objective To share lessons learned from the local experience with teledermatology in Singapore. Methods The main models of teledermatology are asynchronous (store-and-forward), synchronous (real-time communication), and hybrid teledermatology (mixed combination of both asynchronous and synchronous elements). Results During the pandemic, teledermatology has enabled suitable patients to have continued access to clinical care in the comfort of their home, while at the same time supporting safe distancing measures to mitigate exposure to and spread of SARS-CoV-2. At the National Skin Centre in Singapore, asynchronous store-and-forward teledermatology is used for telecollaboration with doctors and nurses from external health care institutions, nursing homes, and primary care clinics. A hybrid model comprising synchronous phone or video teleconsultation with the patient, together with review of recent clinical photographs submitted by the patient, is used for the remote care of selected patients with mild and/or stable dermatological conditions. There is a high diagnostic concordance of 87% between teleconsultation and in-person consultation. As not all patients are suitable for teleconsultation, preteleconsultation triage is helpful. Conclusions Moving forward, even as we approach a new postpandemic era, teledermatology will continue to evolve and become an integral pillar of the health care landscape. 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".