Effect of Patch Testing on the Course of Allergic Contact Dermatitis and Prognostic Factors That Influence Outcomes
Bibliographic record
Abstract
BACKGROUND: Allergic contact dermatitis (ACD) has been shown to adversely affect the quality of life of patients. OBJECTIVE: The aim of the study was to study the effect of patch test on the severity of dermatitis, the quality of life of patients, and the prognostic factors influencing the outcome. METHODS: The study included 111 patients patch tested with the preliminary diagnosis of ACD. Patients with clinically relevant positive patch test reactions were included in the ACD group. All patients were assessed with the Investigator Global Assessment and the Dermatology Quality of Life Index before and 6 months after patch testing. RESULTS: At the sixth-month control, more significant regressions in the mean Investigator Global Assessment and Dermatology Quality of Life Index scores were noted in the ACD group. The allergens were correctly remembered by 75% of the patients. The improvement was more significant in patients with ACD who correctly remembered the allergens and made appropriate lifestyle changes. Multiple allergen positivity was identified as a poor prognostic factor. CONCLUSIONS: The effect of patch test on the prognosis of contact dermatitis depends not only on providing necessary information to patients but also on the number of positive reactions, patient's ability to recall the allergens, how much the avoidance was achieved, and patient-related factors such as sex.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".