Excipient and Dose per Unit Area Affect Sensitivity When Patch Testing with Gold Sodium Thiosulfate
Bibliographic record
Abstract
BACKGROUND: Dose/area and reading paradigms for gold patch testing are controversial and not standardized worldwide. OBJECTIVES: The aims of this study were to determine the optimum patch test dose of gold sodium thiosulfate (GST) in a hydrogel (HYD) and to establish GST HYD safety/efficacy and further characterize normal morphology and time course of GST reactions. METHODS: Twenty gold-allergic patients were patch tested with a dilution series of GST HYD and with GST 2% petrolatum (pet). Furthermore, this previously determined optimal dose was compared with GST 0.5% pet in 19 known-allergic and 216 consecutive subjects. RESULTS: The optimal GST HYD dose was 0.075 mg/cm, not statistically different from GST 2% pet (P = 0.4795). Gold sodium thiosulfate HYD outperformed GST 0.5% pet in both known-allergic subjects (79% vs 63%, P = 0.2482) and consecutive subjects (30% vs 9%, P < 0.0001). Late reactions were common in consecutive patients with both HYD and pet. Significantly more persistent reactions were associated with GST HYD than with GST 0.5% pet. CONCLUSIONS: Gold sodium thiosulfate HYD 0.075 mg/cm is the optimal dose for diagnosis of gold contact allergy with GST. Gold sodium thiosulfate 0.5% pet yielded false-negatives in some patients, suggesting inadequate dose per centimeter squared. Late reads are normal, expected, and necessary for diagnosis of gold contact allergy in this cohort.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".