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Record W2113931572 · doi:10.1177/1203475415586664

Non-melanoma Skin Cancer in Canada Chapter 4: Management of Basal Cell Carcinoma

2015· article· en· W2113931572 on OpenAlexaffabout
David Zloty, Lyn Guenther, Mariusz Sapijaszko, Kirk Barber, Joël Claveau, Tamara Adamek, John Ashkenas Cancer

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsUniversity of CalgaryUniversity of AlbertaWestern UniversityClinique Neuro-OutaouaisUniversity of British Columbia
FundersLEO PharmaGalderma
KeywordsMedicineBasal cell carcinomaSkin cancerDermatologyNatural historyMalignancyCryosurgeryMelanomaCurettageBasal cellMohs surgerySurgeryCancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Basal cell carcinoma (BCC) is the most common malignancy. Growth of BCCs leads to local destruction of neighbouring healthy skin and underlying tissue and can result in significant functional and cosmetic morbidity. OBJECTIVE: To provide guidance to Canadian health care practitioners regarding management of BCCs. METHODS: Literature searches and development of graded recommendations were carried out as discussed in the accompanying Introduction. RESULTS: Although BCCs rarely metastasize, they can be aggressive and disfiguring. This chapter describes the natural history and prognosis of BCCs. Risk stratification is based on clinical features, including the site and size of the tumour, its histologic subtype (nodular vs sclerosing), and its history of recurrence. CONCLUSIONS: Various options should be considered for BCC treatment, including cryosurgery, curettage, and topical or photodynamic approaches, as well as fixed-margin surgery and Mohs micrographic surgery. Stratification of recurrence risk for individual BCCs determines the most appropriate therapeutic course.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.025
GPT teacher head0.253
Teacher spread0.228 · 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
GenreOther

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

Citations38
Published2015
Admission routes2
Has abstractyes

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