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Record W1977865587 · doi:10.2217/fon.10.105

The Potential of Immunomodulatory Drugs in the Treatment of Solid Tumors

2010· review· en· W1977865587 on OpenAlexaboutno aff
Angus Dalgleish, Christine Galustian

Bibliographic record

VenueFuture Oncology · 2010
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSolid tumorIntensive care medicineOncologyPharmacologyInternal medicineCancer

Abstract

fetched live from OpenAlex

Lenalidomide (REVLIMID®) CC-5013 (Celgene, NJ, USA) is approved, in both the USA and Europe, in combination with dexamethasone for the treatment of multiple myeloma patients who have received at least one prior therapy, and is rapidly being accepted worldwide for this condition. Lenalidomide is also approved in the USA and Canada for use in transfusion-dependent anemia in patients with low- and intermediate-1-risk myelodysplastic syndromes associated with del (5q) abnormality with or without additional abnormalities. Lenalidomide is an IMiD® immunomodulatory compound, incorporating structural modification of the drug thalidomide, which is active against a wide variety of autoimmune Th-2-dependent disorders, including erythema nodosum of leprosy, leishmaniasis, as well as severe ulcerative disorders such as Behcet's syndrome. Unfortunately, long-term use of thalidomide is limited, particularly by neurotoxicity. To date, results suggest that lenalidomide is more active than thalidomide and does not cause the neurotoxicity seen with thalidomide. Lenalidomide has multiple properties, including anti-inflammatory, antiangiogenic and costimulatory effects, as well as being able to inhibit T-regulatory cells, all of which are properties deemed desirable for anticancer activity. This article covers the evidence that lenalidomide may have a major role in the treatment and control of many cancer types other than del (5q) myelodysplastic syndrome and multiple myeloma.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.385
Teacher spread0.358 · 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 designOther design
Domainnot available
GenreReview

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

Citations12
Published2010
Admission routes1
Has abstractyes

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