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The differences in human cumulative irritation responses to positive and negative irritant controls from three geographical locations

2008· article· en· W2128197996 on OpenAlexaboutno aff
Mingyi W. Trimble, Nalini Kaul, John E. Wild, James P. Bowman

Bibliographic record

VenueInternational Journal of Cosmetic Science · 2008
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIrritationRelative humidityMedicineImmunologyMeteorologyGeography

Abstract

fetched live from OpenAlex

A retrospective analysis was conducted to evaluate whether studies from three geographically diverse locations have similar response profiles to the positive and negative controls in a standard 14-day cumulative irritation study (1). The positive irritant control (0.1% sodium lauryl sulfate, SLS) and the negative control (0.9% sodium chloride, saline) data from seventeen 14-day cumulative irritation studies were reviewed. The studies were compiled from three locations representing dry/hot, humid/hot, and dry/cold environments (Scottsdale, Arizona; St. Petersburg, Florida; and Winnipeg, Manitoba, respectively). Irritation scores were generated by trained skin graders from a total of 442 subjects studied between 1999 and 2005. Cumulative irritation scores were reviewed and compared between study locations. The irritation scores for the positive and negative controls were not significantly different between locations. Temperature and relative humidity (RH) variation did not correlate significantly with overall irritation. However, the dryer climate (i.e., negative or low dew point) had a tendency to induce a higher overall irritation level for both positive and negative controls.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.174
GPT teacher head0.406
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2008
Admission routes1
Has abstractyes

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