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Record W3167249377 · doi:10.15562/gnc.74

Role of Organ Specific Cancer Stem Cells in Organ Specific Cancer Progression

2020· article· en· W3167249377 on OpenAlexvenueno aff
Muhammad Ali, Fatima Ali, Nadia Wajid

Bibliographic record

VenueJournal of Genes and Cells · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsnot available
Fundersnot available
KeywordsCancer stem cellCancerStem cellCancer cellCancer researchBiologyMedicineInternal medicineCell biology

Abstract

fetched live from OpenAlex

Since the cancer stem cells (CSC) have been identified in 1997 by Bonnet and Dick, more than 100,000 papers have been published on the CSC. Huge research on cancer stem cells helped the scientists to rethink about the cancer therapeutics as classic way of chemotherapy is ineffective because chemotherapy failed to kill these cells, the only reason of cancer relapse. The cancer theory of stem cells is one of the most trending theory in stem cells and cancer biology focusing on the understanding of biology of cancer cells for an enhanced and improved therapeutic approaches should be applied to cure the cancer. This mini-review is a short overview on the role of organ specific cancer stem cells in the organ specific cancer progression.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.275
Teacher spread0.249 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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