Positivity Ratio and Reaction Index: Patch-Test Quality-Control Metrics Applied to the North American Contact Dermatitis Group Database
Bibliographic record
Abstract
BACKGROUND: The positivity ratio (PR) and reaction index (RI) characterize the ability of patch-test preparations to produce strong (++ or +++) reactions as opposed to weak (+), questionable, or irritant reactions. OBJECTIVE: This study evaluates these measures for North American Contact Dermatitis Group (NACDG) patch-test preparations. METHODS: The PR and RI were calculated for 79 NACDG standard allergens tested from 1994 to 2006 (n = 26,479 patients). The median values were used as cutoff values for "acceptable" versus "problematic" preparations. RESULTS: The top 10 "acceptable" patch-test preparations (PR < or = 55 and RI > 0.46) were mixed dialkyl thioureas 1% in petrolatum (pet), tixocortol-21-pivalate 1% pet, ethylenediamine dihydrochloride 1% pet, sesquiterpene lactone mix 0.1% pet, nickel sulfate 2.5% pet, bacitracin 20% pet, thimerosal 0.1% pet, epoxy resin 1% pet, colophony 20% pet, and mercaptobenzothiazole 1% pet. The most "problematic" patch-test preparations (PR > 55 and RI < or = 0.46) were cocamidopropyl betaine 1% aqueous (aq), benzalkonium chloride 0.1% aq, jasmine absolute 2% pet, iodopropynyl butyl carbamate 0.1% pet, 2-bromo-2-nitropropane-1,3-diol 0.5% pet, methyldibromoglutaronitrile 0.4% pet, methyldibromoglutaronitrile/phenoxyethanol 2% pet and 2.5% pet, dimethylol dihydroxyethyleneurea 4.5% aq, and clobetasol-17-propionate 1% pet. CONCLUSION: Caution should be used when interpreting reactions to "problematic" patch-test preparations with a high proportion of weak, irritant, and questionable reactions.
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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".