Overcoming False-Negative Patch Tests: A Systematic Literature Review
Bibliographic record
Abstract
Exogenous allergens, found in cosmetic products, jewelry items, antiseptics and antibacterials, plants, and solvents, can cause clinical allergic contact Dermatitis (ACD). To help identify and discern which allergen is causing ACD, clinicians use patch tests, but they can yield false-negative results at times. Examining potential reasoning for false negatives is particularly helpful when a patient's history and physical examination strongly suggest ACD, and the patch test is negative. A strong history and physical presentation suggestive of ACD warrants additional patch testing or other methods to verify a false-negative patch test result. We conducted a literature review to compile various reasonings and solutions for false-negative patch tests in suspected ACD patients. Utilizing EMBASE, Scopus, PubMed, and Google Scholars, 49 articles were included by using search terms such as "False negative patch test" or "False-negative patch test" and "allergic contact Dermatitis," or "ACD." Common factors that led to false-negative patch test results include low allergen concentration, inadequate percutaneous penetration, technique error, immunosuppressive therapy, and ultraviolet exposure. Potential solutions include using different vehicles, concentration, increasing reading time, repeating the patch test, intradermal testing, and repeat open application testing. If a false-negative patch test is suspected, then intradermal testing can be administered to ensure the specificity of the patch test result. Considering the main contributing factors and solutions to false-negative patch tests, clinicians can accurately diagnose ACD and administer proper treatment plans.
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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".