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Record W2521785556 · doi:10.1007/s13555-016-0145-2

Use of Lesion Response Rate in Actinic Keratosis Trials

2016· article· en· W2521785556 on OpenAlexaff
Rolf‐Markus Szeimies, Petar Atanasov, Robert Bissonnette

Bibliographic record

VenueDermatology and Therapy · 2016
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsInnovaderm (Canada)
FundersGalderma
KeywordsActinic keratosisMedicineContext (archaeology)KeratosisDermatologyClinical trialPerspective (graphical)Medical physicsLesionClinical PracticeConfoundingIntensive care medicineSurgeryPathologyPhysical therapyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Complete patient clearance is often required by regulatory agencies for the approval of treatments for actinic keratosis (AK). However, an increasing number of clinicians have challenged the use of this measure in clinical practice and its interpretation. It has been argued that complete patient clearance often underestimates the clinical benefit of a drug and is influenced by a number of key confounding factors, such as number and distribution of lesions, at baseline. Lesions response rate is one alternative which has been suggested as more relevant due to its applicability to clinical practice and closer reflection of the clinical value of the drug. This paper provides an updated perspective on the topic and details the current thinking on the role of complete clearance and lesion response rate in the context of AK. FUNDING: Galderma.

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 score0.101
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.165
GPT teacher head0.376
Teacher spread0.211 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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