Photographic Documentation and Hand Eczema Severity Index for Severity Assessment of Hand Eczema
Bibliographic record
Abstract
BACKGROUND: Hand eczema (HE) is a fluctuating disease, and an objective assessment of HE severity is coveted. OBJECTIVES: This study was undertaken to test the association between Hand Eczema Severity Index (HECSI) score and panel scores of photographs taken by dermatologists. METHODS: A total of 33 patients with mild HE were included. The patients were part of an intervention study, and HECSI scores and standardized photographs were taken at baseline and at follow-up after 5 months. Actual change in HECSI score was compared with rating of change from photographs. A total of 15 dermatologists were engaged in blinded evaluation of photographs. RESULTS: The highest correlation coefficients between delta HECSI scores and delta panel scores of photographs in the first and second evaluation rounds were found for moderate improvement and moderate worsening, rs = -0.46 (P = 0.009) and 0.52 (P = 0.003), respectively, and major worsening, r = 0.41 (P = 0.021). With respect to minor changes, no statistically significant correlations were found (P > 0.05). CONCLUSIONS: In patients with mild HE, photographic assessment was found useful for major and moderate changes only. Further studies would need to be performed in patients with moderate or severe HE, to evaluate whether clinical photographs are able to capture similar changes as HECSI scores.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".