How will the COVID-19 Pandemic Change Dermatology Services over the next Five Years?
Bibliographic record
Abstract
The advent of COVID-19 has radically transformed conventional affairs in numerous facets of life across the world. The reverberation of such alterations has presented a myriad of challenges to dermatology services worldwide. Dermatology services have attempted to suppress the dissemination of COVID-19 by reducing in-person consultations and non-essential procedures. Teledermatology has been utilised to mediate patient triage to ensure patients are promptly referred to the appropriate service. Additionally, a plethora of cutaneous sequelae of COVID-19 have been identified and exhibit considerable heterogeneity in skin inflammatory findings compared to viral infections with known cutaneous effects. There has been a longstanding demand to efficiently capitalise on limited expertise allied to dermatology services. The COVID-19 pandemic has illuminated the urgent need to extend the dermatological competence of several primary care clinicians. Ultimately, the developing COVID-19 pandemic may provide the impetus to revolutionise dermatology services in the next five years to transcend current challenges in clinical practice.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".