Scalp Allergic Contact Dermatitis: A Retrospective Analysisof Allergen Profiles and Distribution Patterns
Bibliographic record
Abstract
Abstract: Background: Scalp allergic contact dermatitis (ACD) is less frequent than at other body sites, underdiagnosed due to overlapping dermatoses, and understudied. This study characterized its epidemiology, clinical features, allergen profile, lesion distribution, and preexisting scalp conditions. Methods: A retrospective cohort (Sheba Medical Center, 2009–2023) included patients with clinically relevant patch test-confirmed ACD, categorized into 3 groups: ( a ) symptoms only (n = 17), ( b ) visible lesions without preexisting scalp disorder (n = 68), and ( c ) visible lesions with a preexisting scalp disorder (n = 16). Demographic, clinical, and patch test data were analyzed. Results: Scalp ACD represented 3.2% of patch test referrals (n = 101); 91.1% were female (mean age = 50). Common symptoms were itching and hair shedding, erythema, and scaling were the most frequent signs. Lesions involved only the scalp in 45% and extended beyond in 55%. Diagnosis was delayed by an average of 17 months. Patients had an average of 3.45 positive allergens; 50.5% were polysensitized. Nickel sulfate (47.5%), paraphenylenediamine (32%), and methylisothiazolinone/methylchloroisothiazolinone (14%) were most frequent. Allergen patterns varied by lesion distribution and preexisting scalp conditions. Conclusions: Scalp ACD predominantly affects middle-aged women and often extends beyond the scalp. Delayed diagnosis is common. Distinct allergen patterns, frequent polysensitization, and the influence of preexisting conditions highlight the need for targeted allergen avoidance strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".