A Survey Examining Photopatch Test and Phototest Methodologies of Contact Dermatologists in the United States: Platform for Developing A Consensus
Bibliographic record
Abstract
BACKGROUND: There is currently no standardized protocol for photopatch testing and phototesting in the United States. Certain testing paramaters (such as chemicals tested, time between test application and irradiation, and time of final interpretation) vary from provider to provider. These variations may impact comparability and consistency of test results. OBJECTIVE: The goal of our survey-based study was to outline the photopatch test and phototest protocols used by US contact dermatologists. The information obtained will aid in the development of a national consensus on testing methodologies. METHODS: Based on a literature search conducted on differences in testing methodologies, we constructed a questionnaire. The survey was distributed at the American Contact Dermatitis Society annual meeting and via the American Contact Dermatitis Society Web site. Standard descriptive analysis was performed on data obtained. RESULTS: Of the 800 dermatologists contacted, 117 agreed to participate in the survey. Among these respondents, 64 (54.8%) conduct photopatch testing. Results of the survey are presented, and they confirm that a variety of techniques and testing materials are used. CONCLUSIONS: It would be beneficial to enlist a panel of expert contact dermatologists to create by formal consensus, using these research findings, a standard photopatch test protocol for use in this country.
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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 | 0.059 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".