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Record W3042934496 · doi:10.3747/co.27.5371

PD-1 Inhibition in Malignant Melanoma and Lack of Clinical Response in Chronic Lymphocytic Leukemia in the Same Patients: A Case Series

2020· article· en· W3042934496 on OpenAlexaffvenue
Ivan Landego, Donna Hewitt, Irena Hibbert, Dhali H.S. Dhaliwal, W. Pieterse, Debjani Grenier, Ralph Wong, James B. Johnston, Versha Banerji

Bibliographic record

VenueCurrent Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsHealth Sciences CentreCancerCare ManitobaResearch Institute in Oncology and HematologyUniversity of Manitoba
Fundersnot available
KeywordsMedicineChronic lymphocytic leukemiaMelanomaOncologyMalignancyInternal medicineLeukemiaCancerPopulationImmunologyCancer research

Abstract

fetched live from OpenAlex

Chronic lymphocytic leukemia (cll) is the most common adult leukemia in the Western world. Unfortunately, affected patients are often immunosuppressed and at increased risk of infection and secondary malignancy. Previous meta-analysis has found that patients with cll have a risk of melanoma that is increased by a factor of 4 compared with the general population. Recent advances in the understanding of the PD receptor pathway have led to immunotherapies that target cancer cells. The use of PD-1 inhibitors is now considered first-line treatment for BRAF wild-type metastatic melanoma. Interestingly, early preclinical data suggest that inhibition of that pathway could also be used in the treatment of cll; however, recent clinical data did not support the effectiveness of that approach. In this case series, we highlight 2 cases in which patients with cll and concurrent malignant melanoma underwent treatment with PD-1 inhibitors and were found to experience reductions in their white blood cell counts without improvement in their hemoglobin. Those cases further illustrate that treatment of cll with PD-1 inhibitors is ineffective.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.198
GPT teacher head0.461
Teacher spread0.262 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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