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Record W2903302517 · doi:10.18192/osurj.v1i1.3687

Do stem cell divisions significantly contribute to cancer development?

2018· article· en· W2903302517 on OpenAlexaffvenue
Ryan Sandarage, Justin G. Chitpin

Bibliographic record

VenueUniversity of Ottawa Science Undergraduate Research Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCancer stem cellCancerLuckStem cellCancer cellBiologyCancer preventionCancer researchGeneticsEpistemology

Abstract

fetched live from OpenAlex

Cancer is caused by uncontrolled cellular growth, yet some cancer cells may have greater roles in sustaining tumour proliferation and overcoming conventional cancer treatments. The origins of these cancer stem cells, as they are hypothesized, is widely contested. The current opinion points to extrinsic factors such as smoking, diet and sedentary lifestyle to be the primary inducer of cancer stem cells. This dogma was challenged in 2015 when Tomasetti and Vogelstein postulated that two-thirds of the variation in cancer risk could be explained by random mutations arising during DNA replication in healthy stem cells. Their "bad luck" hypothesis sparked fierce debate and controversy in the scientific community. In this point-counterpoint article, we discuss the random mutation cancer stem cell model and its implications for guiding public health through primary and secondary cancer prevention.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.315
Teacher spread0.283 · 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 designTheoretical or conceptual
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
Published2018
Admission routes2
Has abstractyes

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