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Record W4392866757 · doi:10.1093/bjd/ljae065

A guide to improving the design and analysis of observational studies on the long-term safety of topical corticosteroids

2024· article· en· W4392866757 on OpenAlexaff
Aaron M. Drucker, Peter C. Austin, Jane Harvey, S. Lax, Mina Tadrous, Kim S Thomas

Bibliographic record

VenueBritish Journal of Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsUniversity of TorontoWomen's College Hospital
Fundersnot available
KeywordsObservational studyConfoundingMedicineTerm (time)Intensive care medicineRisk analysis (engineering)Pathology

Abstract

fetched live from OpenAlex

Despite a long track-record of use, patients and clinicians continue to have concerns about the safety of topical corticosteroids (TCS). Observational studies in routinely collected health data provide an opportunity to address those concerns but are challenging to conduct in a way that minimizes bias and confounding. We review challenges and potential solutions for the conduct of observational studies on the long-term safety of TCS.

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)0.243
metaresearch head score (Gemma)0.434
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.757
Threshold uncertainty score0.933

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2430.434
Meta-epidemiology (narrow)0.0040.007
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0120.011
Science and technology studies0.0020.003
Scholarly communication0.0060.005
Open science0.0080.004
Research integrity0.0080.014
Insufficient payload (model declined to judge)0.0500.035

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 head0.055
GPT teacher head0.341
Teacher spread0.287 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations1
Published2024
Admission routes1
Has abstractyes

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