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Record W3049199966 · doi:10.14740/jh689

Impact of Opioid Use in Hematological Malignancies: Clinical, Immunological and Concomitant Aspects

2020· review· en· W3049199966 on OpenAlexvenueno aff
Polina Tregubenko, Valeriy Zvonarev

Bibliographic record

VenueJournal of Hematology · 2020
Typereview
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImmunosuppressionConcomitantCancerOpiateOpioidCancer painImmunologyOncologyInternal medicine

Abstract

fetched live from OpenAlex

Opioid agents play a unique role in pain and symptom management for cancer patients. Research shows that opiate use, especially when associated with underlying cancer, has significant effects on hematological parameters. These changes may lead to greater risk for immunosuppression, tumor growth and progression of metastatic processes. The aim of this review is to explore the effects of opiates on various metabolic and biological processes, as well as the hematopoietic system, especially in cancer patients. Our findings demonstrate that the tumor-promoting effects of opiates remain contradictory, as both growth-promoting and anti-tumor effects have been observed. However, available data suggest that opiates can facilitate the proliferation and migration of tumor cells, and understanding of this process on cancer treatment is tremendously important.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.408
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

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