Inter‐rater variability in patch test readings and final interpretation using store‐forward teledermatology
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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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 | 0.039 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".