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Record W3124208607 · doi:10.3389/fmed.2020.637223

Muffin Technique Micrographic Surgery for Non-melanoma Skin Cancer

2021· article· en· W3124208607 on OpenAlexaffabout
Philip Surmanowicz, Arunima Sivanand, Amy Du, Muhammad N. Mahmood, Robert Gniadecki

Bibliographic record

VenueFrontiers in Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineBasal cell carcinomaSkin cancerBasal cellMelanomaSurgeryDermatologyCancerRetrospective cohort studyMargin (machine learning)Single CenterPathologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Mohs micrographic surgery (MMS) is the gold standard treatment for high-risk facial non-melanoma skin cancer. However, patients' access to MMS is limited by cost. The muffin technique micrographic surgery (MTMS) is an alternative micrographic technique wherein the entire excised margin is evaluated post-operatively by a pathologist using paraffin-embedded material. Herein, we describe the implementation and the preliminary results of MTMS in an academic dermatology center. Objective: To describe the MTMS and outline its efficacy and safety in a real-world clinical academic setting. Methods: A retrospective chart review was conducted of all patients with basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) who underwent MTMS at the University of Alberta Dermatology Center from June 2016 until July 2019. Results: A total of 69 patients were included (64 BCCs and 5 SCCs). 68.1% of surgeries had clear margins following the first incision, 100% after second round re-excisions. There were no observed cases of tumor recurrence after a median 40 months of follow-up. There were no major adverse events or complications. Conclusions: MTMS is a superior alternative to simple excision of skin cancer by providing full margin control and residual tumor mapping.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.016
GPT teacher head0.292
Teacher spread0.276 · 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.

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

Citations8
Published2021
Admission routes2
Has abstractyes

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