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Record W2129695168 · doi:10.1177/1203475415588652

Non-melanoma Skin Cancer in Canada Chapter 1: Introduction to the Guidelines

2015· article· en· W2129695168 on OpenAlexaffabout
Lyn Guenther, Kirk Barber, Gordon E. Searles, Charles Lynde, Peter M. Janiszewski, John Ashkenas

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsLynde Centre for DermatologyUniversity of TorontoUniversity of CalgaryGuenther Dermatology Research CentreWestern University
Fundersnot available
KeywordsMedicineSkin cancerActinic keratosesDermatologyBasal cell carcinomaGrading (engineering)Basal cellMelanomaCancerPathologyCancer researchInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Non-melanoma skin cancer (NMSC), including basal and squamous cell carcinoma, represents the most common malignancy. OBJECTIVE: The aim of this document is to provide guidance to Canadian health care practitioners on NMSC management. METHODS: After conducting a literature review, the group developed recommendations for prevention, management, and treatment of basal cell carcinomas, squamous cell carcinomas, and actinic keratoses. These tumour types are considered separately in the accompanying articles. The Grading of Recommendations Assessment, Development and Evaluation system was used to assign strength to each recommendation. RESULTS: This introduction describes the scope and structure of the guidelines and the methods used to develop them. The epidemiology of NMSC is reviewed, as are the pathophysiologic changes occurring with damage to the skin, which lead to the formation of actinic keratoses and invasive squamous or basal cell carcinomas. CONCLUSIONS: This introduction describes the need for primary prevention and offers an overview of treatment options that are discussed in later chapters of the guidelines.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.748

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 head0.045
GPT teacher head0.301
Teacher spread0.256 · 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

Citations14
Published2015
Admission routes2
Has abstractyes

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