Bibliographic record
Abstract
This article discusses the future of allergic contact dermatitis (ACD) diagnosis and management. Due to early landmark discoveries in this disorder, there is a knowledge base that can be refined. Improved patch-test technology, patch-test strategy, and patch-testing clinical relevance are emphasized. Other factors for improvement include complete ingredient identification on Material Safety Data Sheets, knowledge of the relationship of extracutaneous allergic disease to ACD, understanding of the evolutionary implications of ACD, increased chemical identification of allergens in publication, and enhanced knowledge of immunologic contact urticaria and irritant dermatitis syndrome. Further research into in vitro diagnostic methods, skin bioengineering tools, and ribonucleic acid diagnostic methods may lead to more precise and less invasive diagnosis of ACD. Development of national and international allergen banks and analytic chemistry laboratories would allow better documentation and epidemiology of allergens, as would collaboration between national and international contact dermatitis organizations via the World Wide Web. Finally, additional fellowships in contact allergy should produce more dermatologists equipped to face the complex challenges of ACD and develop solutions for ongoing questions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".