Inter‐rater variability in patch test readings and final interpretation using store‐forward teledermatology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Data regarding teledermatology for patch testing are limited. OBJECTIVES: Compare patch test readings and final interpretation by two in-person dermatologists (IPDs) with eight teledermatologists (TDs). METHODS: Patch tested patients had photographs taken of 70 screening series of allergens at 48 hours and second readings. Eight TDs reviewed photos and graded reactions (negative, irritant, doubtful, +, ++, +++) at 48 hours and second readings; in addition, they coded a final interpretation (allergic, indeterminant, irritant, negative) for each reaction. TDs rated overall image quality and confidence level for each patient and patch test reaction, respectively. Percentage of TD-IPD agreement based on clinical significance (success, indeterminate, and failure) was calculated. Primary outcome was agreement at the second reading. RESULTS: Data were available for 99, 101, and 66 participants at 48 hours, second reading, and final interpretation, respectively. Pooled failure (+/++/+++ vs negative) at second reading was 13.6% (range 7.9%-20.4%). Pooled failure at 48 hours and final interpretation was 5.4% (range 2.9%-6.8%) and 24.6% (range 10.2%-36.8%), respectively. Confidence in readings was statistically correlated with quality of images and disagreement. CONCLUSION: For patch testing, teledermatology has significant limitations including clinically significant pooled failure percentages of 13.6% for second readings and 24.6% for final interpretation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it