Contact Sensitivity in Patients With Leg Ulcerations
Bibliographic record
Abstract
OBJECTIVES: (1) To determine the prevalence of allergen sensitivity in patients with past or present leg ulcers in 2 North American study centers vs European study findings and the North American Contact Dermatitis Group (NACDG) database and (2) to delineate a standard battery of allergens for patch testing in North American patients that is representative of the newer dressings and wound care products. DESIGN: Fifty-four patients, with or without dermatitis, were prospectively entered in the study. The patients were patch tested to the NACDG standard series and a comprehensive supplemental series of 52 allergens. SETTING: Wound healing clinics at Boston University Roger Williams Medical Center and University of Ottawa. RESULTS: Sixty-three percent (n = 34) of patients had 1 or multiple positive patch test results, and 37% (n = 20) had no positive patch test result. The most common allergens were Myroxylon pereirae (balsam of Peru) (30% [16/54]), bacitracin (24% [13/54]), fragrance mix (20% [11/54]), wood tar mix (20% [11/54]), propylene glycol (14% [7/52]), neomycin sulfate (13% [7/54]), benzalkonium chloride (13% [7/54]), carba mix (11% [6/54]), nickel sulfate (11% [6/54]), and control gel hydrocolloid (11% [6/54]). CONCLUSIONS: Comparable to European study findings, there is a high incidence of positive patch test results in patients with past or present leg ulcerations. The incidences of the most common allergens in our patient population were higher than those seen in the NACDG, except for nickel. Using a modified leg ulcer series along with the standard NACDG series is important in evaluating patients with leg ulcers.
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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 | 0.002 |
| 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) | 0.003 | 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".