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Record W3042704706 · doi:10.1002/jso.26121

Lesional therapies for in‐transit melanoma

2020· article· en· W3042704706 on OpenAlexaff
Ashlie Nadler, Nicole J. Look Hong, Nasrin Alavi, Wadid Abadir, Frances C. Wright

Bibliographic record

VenueJournal of Surgical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineImiquimodCommon Terminology Criteria for Adverse EventsMelanomaInternal medicineDermatologySurgeryRetrospective cohort studyToxicityAdverse effectOncology

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: To describe the outcomes of lesional therapy of in-transit melanoma (ITM) with interleukin-2 (IL-2), diphencyprone (DPCP), combination lesional therapy (IL-2, retinoid, and imiquimod; CLT), and imiquimod. METHODS: Data was collected for consecutive patients with ITM receiving lesional therapies from 2008 to 2018 in a retrospective review. Included patients did not have metastatic disease at time of starting on lesional therapy and were not on systemic therapy. The primary outcome was complete pathologic response (pCR). RESULTS: Of 83 patients, 57 (69%) started treatment with IL-2, 10 (12%) with DPCP, 12 (14%) with CLT, and 4 (5%) with imiquimod. pCR was achieved in 34 patients (41%) overall, including 44% starting on IL-2, 20% on DPCP, 58% on CLT, and none on imiquimod (P = .024). With a median follow-up of 45 months, cumulative one-year overall survival was 86%, with the best survival in the CLT group. Forty-eight percent experienced common terminology criteria for adverse events grade 1 or 2 toxicity. A quarter of patients on DPCP discontinued therapy due to toxicity (P = .002). CONCLUSIONS: IL-2 may be considered for the treatment of ITM with multiple or rapidly developing lesions where there would otherwise be significant morbidity with surgery, given pCR rates and toxicity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.470

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 head0.041
GPT teacher head0.316
Teacher spread0.275 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

Explore more

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