Updates to the Research Institute for Fragrance Materials, Inc. Confirmation of No Induction in Human Test Standard Protocol
Bibliographic record
Abstract
Abstract: Background: At the recommendation of the Expert Panel for Fragrance Safety, the Research Institute for Fragrance Materials, Inc. (RIFM) considered adopting a 48-hour challenge for confirmation of no induction in human (CNIH) studies in order to more closely mimic the patch testing conducted by dermatologists assessing allergic contact dermatitis. Objectives: A pilot study was undertaken to ensure that adopting a new protocol would not invalidate the 30+ years of human patch-testing data collected by RIFM. Methods: Two protocols were simultaneously tested to determine if a 48-hour challenge patching would result in reactions significantly different from those produced during a 24-hour challenge patching. RIFM tested 19 fragrance materials, including key ingredients in natural complex substances. Conclusions: Although patching for 48 hours is more sensitive than patching for 24 hours, during this pilot, no significant differences in sensitization were noted between the two challenge protocols when 2317 subjects were tested with 19 test materials, a vehicle control, and a saline control. Therefore, adopting this new 48-hour challenge patching for RIFM-conducted CNIHs does not invalidate previously published studies conducted according to the 2008 RIFM standard protocol, which utilized a 24-hour patching during the challenge phase.
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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 | 0.041 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.071 | 0.056 |
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".