Optimizing Reproducibility for Clinical Studies Involving Patch Testing and Application of Topical Preparations
Bibliographic record
Abstract
BACKGROUND: The reproducibility and consistency of patch-test techniques can be problematic, making controlled clinical trials of patch testing difficult. OBJECTIVE: To measure the accuracy and reproducibility of applying controlled quantities of petrolatum onto Finn Chambers. METHODS: Four dermatology nurses applied a total of 240 samples of white petrolatum, using three syringe sizes and types. Three different amounts of white petrolatum (0.02 mL, 0.05 mL, and the "usual" amount) were expressed onto previously weighed Finn Chambers, using three different syringe sizes (5 mL, 1 mL, and 0.5 mL), five times each on two separate days. RESULTS: The average weights of 0.05 mL of petrolatum expressed with each type of syringe (5 mL: 0.04252 g; 1 mL: 0.04084 g; and 0.5 mL: 0.04139 g) were not significantly different from each other in pairwise comparisons (p > .36) or from the "gold standard" expected value (0.04138 g, p > .72). The average weights of 0.02 mL of petrolatum expressed with two types of syringes (5 mL: 0.02138 g; 0.5 mL: 0.01778 g) were significantly different from each other (p = .0012), but neither differed significantly from the expected value (0.01655 g, p > .08). The variance due to the effect modifications of nurse, day, and interaction of nurse and day was statistically significant for measurements made with the 5 mL syringe but not for measurements made with the 1 mL or 0.5 mL syringe. CONCLUSION: The average amounts of petrolatum extruded from smaller syringes (0.5 mL and 1 mL) were less variable and more reproducible than those extruded from a 5 mL syringe.
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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 | 0.166 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".