Methylchloroisothiazolinone/Methylisothiazolinone and Methylisothiazolinone Allergies Can Be Detected by 200 ppm of Methylchloroisothiazolinone/Methylisothiazolinone Patch Test Concentration
Bibliographic record
Abstract
BACKGROUND: Methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) and methylisothiazolinone (MI) contact allergies are rising dramatically. Moreover, 100 ppm of MCI/MI patch test might not detect an important number of MCI/MI and MI allergies. OBJECTIVES: This study aimed to present the prevalence of contact allergy to both preservatives in an area of Spain and to investigate if 100 ppm of MCI/MI is an adequate concentration for a proper diagnosis. METHODS: A prospective study was conducted from October 2011 to September 2013. All patients were patch tested with the Spanish baseline series (containing 100 ppm of MCI/MI) and with 200 ppm of MCI/MI and 2000 ppm of MI. RESULTS: A total of 490 patients were patch tested. The MCI/MI prevalence was 10% and increased from 7.8% in last term of 2011 to 14.3% in the first 9 months of 2013. The MI prevalence was 4.5% and increased from 1% to 7.7% in the same period. One hundred parts per million of MCI/MI could not diagnose 24.5% of MCI/MI allergies. All MI allergies were detected by 200 ppm of MCI/MI, whereas only 68.2% were positive to 100-ppm concentration. CONCLUSIONS: For a correct diagnosis of MCI/MI and MI contact allergies, we advocate increasing the MCI/MI patch test concentration to 200 ppm along with a temporal inclusion of MI in the North American Contact Dermatitis Group baseline series.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".