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Record W1967915634 · doi:10.1111/1346-8138.12195

Fixed eruption due to quinine in tonic water: A case report with high‐performance liquid chromatography and ultraviolet <scp>A</scp> analyses

2013· article· en· W1967915634 on OpenAlexaboutno aff
Aoi Ohira, Sayaka Yamaguchi, Takuya Miyagi, Yu-ichi YAMAMOTO, Satoshi Yamada, Hideo Shiohira, Keisuke Hagiwara, Tsukasa Uno, Hiroshi Uezato, Kenzo Takahashi

Bibliographic record

VenueThe Journal of Dermatology · 2013
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsErythemaQuinineTonic (physiology)UltravioletHigh-performance liquid chromatographyUltraviolet lightChemistryMedicineFood scienceDermatologyAnesthesiaChromatographyInternal medicinePathologyMalariaPhotochemistryMaterials science

Abstract

fetched live from OpenAlex

Fixed drug eruption is a common cutaneous adverse reaction in young patients with a characteristic clinical appearance. However, the diagnosis and identification of the substance may be difficult if food or food additives provoke the fixed eruption. A 26-year-old man had a history of two episodes of cutaneous erythema with residual pigmentation. Close examination of the history including his diet in addition to an oral challenge test and patch testing led to the diagnosis of fixed eruption secondary to quinine in tonic water. We examined for the presence of quinine in commercially available brands of tonic water using ultraviolet A and irradiation and high-performance liquid chromatography. Both Schweppes and CANADA DRY brands of tonic water emitted fluorescent light upon ultraviolet A irradiation, and contained quinine at concentrations of 67.9 and 61.3 mg/L, respectively. Quinine contained in some tonic waters may trigger fixed eruption.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.284
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations20
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of DermatologySame topicDrug-Induced Adverse ReactionsFrench-language works237,207