Quality of life measurement in teledermatology. Position statement of the European Academy of Dermatology and Venereology Task Forces on Quality of Life and Patient Oriented Outcomes and Teledermatology
Bibliographic record
Abstract
Many events, including the COVID-19 pandemic, have accelerated the implementation of teledermatology pathways within dermatology departments and across healthcare organizations. Quality of Life (QoL) assessment in dermatology is also a rapidly developing field with a gradual shift from theory to practice. The purpose of this paper organized jointly by the European Academy of Dermatology and Venereology (EADV) Task Force (TF) on QoL and patient-oriented outcomes and the EADV TF on teledermatology is to present current knowledge about QoL assessment during the use of teledermatology approaches, including data on health-related (HR) QoL instruments used in teledermatology, comparison of influence of different treatment methods on HRQoL after face-to-face and teledermatology consultations and to make practical recommendations concerning the assessment of QoL in teledermatology. The EADV TFs made the following position statements: HRQoL assessment may be an important part in most of teledermatology activities; HRQoL assessment may be easily and effectively performed during teledermatology consultations. It is especially important to monitor HRQoL of patients with chronic skin diseases during lockdowns or in areas where it is difficult to reach a hospital for face-to-face consultation; regular assessment of HRQoL of patients with skin diseases during teledermatology consultations may help to monitor therapy efficacy and visualize individual patient's needs; we recommend the use of the DLQI in teledermatology, including the use of the DLQI app which is available in seven languages; it is important to develop apps for dermatology-specific HRQoL instruments for use in children (for example the CDLQI and InToDermQoL) and for disease-specific instruments.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".