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Record W3171944929 · doi:10.46747/cfp.6706414

Approach to allergic contact dermatitis caused by topical medicaments

2021· article· en· W3171944929 on OpenAlexaffvenueabout
Charles Q. Choi, Saba Vafaei‐Nodeh, Jamie Phillips, Gillian de Gannes

Bibliographic record

VenueCanadian Family Physician · 2021
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineMedical prescriptionAllergic contact dermatitisDermatologyContact dermatitisAllergyPatch testingDrug eruptionDrugImmunologyPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide an approach to identifying topical medicament ingredients that cause allergic contact dermatitis (ACD) and to recognizing common clinical scenarios in which these ingredients might present. SOURCES OF INFORMATION: A retrospective chart review was conducted of patients patch tested at the Contact Dermatitis Clinic at St Paul's Hospital in Vancouver, BC, between November 2016 and June 2019. Data from the North American Contact Dermatitis Group from 2015 to 2016 and The Ottawa Hospital patch test clinic from 2000 to 2010 were also reviewed. MAIN MESSAGE: Topical antibiotics are the most common cause of ACD to medicaments and frequently cause cosensitization to multiple allergens. This hypersensitivity reaction is often seen following surgical procedures and should be distinguished from postoperative infection. Corticosteroid allergy is easy to miss and should be suspected in cases of corticosteroid-sensitive dermatoses that worsen despite appropriate treatment. Topical anesthetics and propylene glycol are other causes of ACD found in many prescription and over-the-counter products. CONCLUSION: Allergic contact dermatitis is easy to miss and should always be considered in cases of eczematous eruptions. A thorough drug history including all topical products-both prescription and over-the-counter-is critical. Patch testing can help identify specific allergens for the patient to avoid.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.678
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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 designNot applicable
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

Citations10
Published2021
Admission routes3
Has abstractyes

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