MétaCan
Menu
Back to cohort
Record W2169080684 · doi:10.1177/1203475415583414

Non-melanoma Skin Cancer in Canada Chapter 3: Management of Actinic Keratoses

2015· article· en· W2169080684 on OpenAlexaffabout
Yves Poulin, Charles Lynde, Kirk Barber, Ronald Vender, Joël Claveau, Marc Bourcier, John Ashkenas

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsUniversité de SherbrookeMcMaster UniversityDermatrials ResearchUniversité LavalLynde Centre for DermatologyUniversity of TorontoClinique Neuro-OutaouaisUniversity of CalgaryCentre de Recherche Dermatologique du Québec Métropolitain
FundersLEO Pharma
KeywordsMedicineActinic keratosisActinic keratosesDermatologySkin cancerKeratosisImmunosuppressionTreatment modalityMelanomaPhotodynamic therapyBasal cellCancerSurgeryPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Actinic keratosis (AK) and cheilitis (AC) are lesions that develop on photodamaged skin and may progress to form invasive squamous cell carcinomas (SCCs). OBJECTIVE: To provide guidance to Canadian health care practitioners regarding management of AKs and ACs. METHODS: Literature searches and development of graded recommendations were carried out as discussed in the accompanying introduction (chapter 1 of the NMSC guidelines). RESULTS: Treatment of AKs allows for secondary prevention of skin cancer in sun-damaged skin. Because it is impossible to predict whether a given AK will regress, persist, or progress, AKs should ideally be treated. This chapter discusses options for the management of AKs and ACs. CONCLUSIONS: Treatment options include surgical removal, topical treatment, and photodynamic therapy. Combined modalities may be used in case of inadequate response. AKs are particularly common following the long-term immunosuppression in organ transplant patients, who should be monitored frequently to identify emerging lesions that require surgery.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.034
GPT teacher head0.283
Teacher spread0.249 · 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

Citations29
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Cutaneous Medicine and SurgerySame topicNonmelanoma Skin Cancer StudiesFrench-language works237,207