Self-reported versus physician-reported severity of hand eczema - Concordance analysis based on data from the German CARPE registry
Bibliographic record
Abstract
Abstract Background and Objective: Self-assessment of general health status includes a variety of verifiable objective and subjective criteria and shows a significant influence on patient-related outcomes such as quality of life. The objective of this study was to investigate how patients assess the severity of their chronic hand eczema compared to physicians.Patients and method: Data for 1281 patients and their physicians from the German registry “CARPE” was available at baseline. At a 2-year follow-up, the remaining 788 cases served as a comparison. Concordance analysis was performed using Bangdiwala's B.Results:Only 16.62% (baseline) and 11.47% (follow-up) of the severity ratings showed complete concordance. The baseline shows a more pessimistic, the follow-up a more optimistic assessment from the patients compared to the physicians. Women and older patients had lower agreement. Bangdiwala's B value lies at the lower border of a substantial agreement ( B̂ω(baseline)=0.573, B̂ω(follow-up)=0.498).Conclusion:The assessment of the severity of hand eczema often differs between physician and patient. To ensure successful therapy and adherence, dermatologists should inquire about their patients' individual perception of the severity of the hand eczema, especially in older patients and women.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".