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Record W4407133484 · doi:10.1097/cmr.0000000000001025

Diagnosis and management of concurrent metastatic melanoma and chronic myelomonocytic leukemia

2025· article· en· W4407133484 on OpenAlex
Kishan Bhatt, Anna Vaynrub, Jason Cham, Sunil Iyer, Benjamin Izar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMelanoma Research · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsColumbia College
FundersNational Cancer Institute
KeywordsMedicineChronic myelomonocytic leukemiaAzacitidineMelanomaChronic lymphocytic leukemiaOncologyInternal medicineVenetoclaxAdverse effectLeukemiaIncidence (geometry)Cancer researchMyelodysplastic syndromes

Abstract

fetched live from OpenAlex

While the association between chronic lymphocytic leukemia (CLL) and a higher incidence of melanoma is well documented, the diagnosis of concurrent high-risk chronic myelomonocytic leukemia (CMML) and metastatic melanoma (MM) has not previously been described. Moreover, the treatment of MM and CMML differ greatly in the mechanism of action of their corresponding antineoplastic therapies: treatment of MM frequently involves immune checkpoint inhibitors (ICI), while patients with CMML receive myelosuppressive agents. Simultaneous management of these malignancies can be nuanced due to the potential impact of one treatment's constituents on the activity of the other and the broad and nonoverlapping array of potential adverse effects of these agents. Here, we describe the clinical course of a patient who was diagnosed with concurrent MM and CMML and our approach to the challenging balance of delivering ICI concurrently with the hypomethylating agent azacitidine and the BCL-2 inhibitor venetoclax.

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.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.070
GPT teacher head0.400
Teacher spread0.330 · 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