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Record W2155999581 · doi:10.1186/1710-1492-9-24

CSACI position statement: safety of topical calcineurin inhibitors in the management of atopic dermatitis in children and adults

2013· article· en· W2155999581 on OpenAlexaffvenueabout
Audrey O. Segal, Anne K. Ellis, Harold L Kim

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2013
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsMcMaster UniversityWestern UniversityQueen's University
Fundersnot available
KeywordsPimecrolimusAtopic dermatitisCalcineurinMedicineTacrolimusDermatologySurgeryTransplantation

Abstract

fetched live from OpenAlex

Atopic dermatitis (AD) is a condition frequently encountered in medical practices across the country. Arming ourselves with appropriate and safe treatment modalities to provide relief for this chronic and relapsing inflammatory condition is of utmost importance to our patients and their families. Utilizing topical calcineurin inhibitors (TCIs) for the treatment of AD not responsive to high-potency corticosteroids, or low-potency corticosteroids and localized to the face, eyelids, and skin folds of patients >2 years, is reasonable to include in common practice. Despite the FDA's Black Box warning, to date no evidence has been published linking the TCIs to an increased incidence of malignancy in either children or adults that establishes causation. The Canadian Society of Allergy and Clinical Immunology (CSACI) therefore recognizes that the benefits of TCIs should be carefully weighed with the theoretical risks in advising patients, and acknowledges that long-term studies remain in progress. The safety and efficacy of topical tacrolimus and pimecrolimus should therefore be considered when treating children and adults with AD in Canadian allergy and immunology practices.

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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0150.009
Insufficient payload (model declined to judge)0.0060.004

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 head0.007
GPT teacher head0.275
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations28
Published2013
Admission routes3
Has abstractyes

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