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Record W4238607270 · doi:10.1177/1203475415582318

Non-melanoma Skin Cancer in Canada Chapter 5: Management of Squamous Cell Carcinoma

2015· article· en· W4238607270 on OpenAlexaffabout
Mariusz Sapijaszko, David Zloty, Marc Bourcier, Yves Poulin, Peter M. Janiszewski, John Ashkenas

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsSKiN HealthUniversity of British ColumbiaCentre de Recherche Dermatologique du Québec MétropolitainUniversité de SherbrookeUniversité LavalUniversity of Alberta
FundersLEO PharmaF. Hoffmann-La Roche
KeywordsMedicineSkin cancerMelanomaRadiation therapyCancerDermatologyBasal cellBasal cell carcinomaNatural historySurgeryPathologyInternal medicineCancer research

Abstract

fetched live from OpenAlex

BACKGROUND: Squamous cell carcinoma (SCC) is the second-most common form of non-melanoma skin cancer (NMSC). OBJECTIVE: To provide guidance to Canadian health care practitioners regarding management of SCCs. METHODS: Literature searches and development of graded recommendations were carried out as discussed in the accompanying introduction (chapter 1 of the NMSC guidelines). RESULTS: SCCs are sometimes confined to the epidermis, but they can also invade nearby tissues and, in some cases, metastasize to neighbouring lymph nodes or other organs. This chapter discusses the natural history, staging, prognosis, and management of SCC--a tumour type that is less common but typically more aggressive than BCC. For this reason, margin control is strongly preferred in treating SCCs. CONCLUSIONS: Although approaches such as cryosurgery and radiation therapy may be considered for some patients, surgical excision--sometimes coupled with radiation--remains the cornerstone of SCC management. Patients with high-risk SCC may also be considered for referral to an appropriate multidisciplinary clinic.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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 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

Citations39
Published2015
Admission routes2
Has abstractyes

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