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Record W2799562727 · doi:10.1097/dss.0000000000001498

Treatment for Lentigo Maligna of the Head and Neck: Survey of Practices in Ontario, Canada

2018· article· en· W2799562727 on OpenAlexaffabout
Annie Liu, Alexis Botkin, Christian Murray, David P. Goldstein, Stefan O.P. Hofer, Nowell Solish, Jessica Kitchen, An‐Wen Chan

Bibliographic record

VenueDermatologic Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsUniversity Health NetworkWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsLentigo malignaMedicineLentigo maligna melanomaHead and neckCheekEyelidSurgerySurgical marginRejuvenationDermatologyMelanomaResection

Abstract

fetched live from OpenAlex

BACKGROUND: Lentigo maligna is an in situ form of cutaneous melanoma that commonly arises on the head and neck. Various surgical and nonsurgical treatment options are available but no randomized trials exist to guide practice. OBJECTIVE: To determine the current treatment practices for lentigo maligna of the head and neck in Ontario, Canada. MATERIALS AND METHODS: Cross-sectional survey of dermatologists, plastic surgeons, and head and neck surgeons. RESULTS: The response rate was 35% (190/542). Wide excision with immediate reconstruction was the most commonly recommended treatment for tumors on the cheek (69%), whereas staged excision with margin control was recommended most often for tumors on the nasal ala (60%). Overall, 5 mm was the most frequently recommended initial surgical margin (69%); 26.5% of respondents recommended margins wider than 5 mm. For tumors on the nasal ala, eyelid, and ear helix, more than 30% of respondents recommended an initial margin narrower than 5 mm. CONCLUSION: Although surgical excision is the predominant treatment modality for lentigo maligna on the head and neck, practices vary considerably in terms of the type of excision and the initial margin used. Potential response bias and the geographic restriction of our sample may limit the generalizability of our results.

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 score0.049
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.081
GPT teacher head0.294
Teacher spread0.213 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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