Patch-Test Reactions to Formaldehydes, Bioban, and Other Formaldehyde Releasers
Bibliographic record
Abstract
BACKGROUND: Contact allergy to formaldehyde, Bioban, and other formaldehyde releasers and cross-reactivity between them have been reported in the literature; however, not many studies have data on this cross-reactivity. OBJECTIVE: To study (1) the rates of allergy to formaldehyde and to Bioban and other formaldehyde releasers and (2) the rates of cross-reactivity between them. METHODS: We present a retrospective chart analysis of patch-test results for all patients referred for allergic contact dermatitis testing at the Milton S. Hershey Medical Center from June 2004 to September 2005. Anyone allergic to formaldehyde, Bioban, or other formaldehyde releasers was included. Cross-reactivity between the agents was then analyzed. RESULTS: The charts of 210 patients were analyzed. Of these patients, 24 (11%) were allergic to formaldehyde, Bioban, or other formaldehyde-releasing agents. Seventeen (8.1%) of the patients were allergic to formaldehyde, 15 (7.1%) were allergic to Bioban, and 20 (9.5%) were allergic to other formaldehyde-releasing agents. Eleven (65%) of the 17 formaldehyde-allergic patients were also allergic to Bioban. Of the 20 patients allergic to formaldehyde-releasing agents, 14 (70%) were also allergic to one of the three Bioban products tested. Of the 15 patients allergic to Bioban, 11 (73%) were allergic to formaldehyde, 14 (93%) were allergic to formaldehyde-releasing agents, and 11 (73%) were allergic to both formaldehyde and formaldehyde-releasing agents. CONCLUSION: A high cross-reactivity rate between formaldehyde, Bioban, and other formaldehyde-releasing agents was found.
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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 | 0.000 |
| 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) | 0.006 | 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".