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Record W1926841542 · doi:10.1111/ctr.12559

Mammalian target of rapamycin inhibition after solid organ transplantation: <i>can</i> it, and <i>does</i> it, reduce cancer risk?

2015· review· en· W1926841542 on OpenAlexafffund
Mamatha Bhat, Kymberly D. Watt

Bibliographic record

VenueClinical Transplantation · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineEverolimusSirolimusImmunosuppressionMalignancyTransplantationOrgan transplantationSolid organCancerCancer researchOncologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

The mammalian target of rapamycin (mTOR) inhibitors sirolimus and everolimus has been increasingly used as immunosuppressants for recipients of solid organ transplants. Over the years, potential advantages unique to this class of immunosuppressants have been recognized, including chemoprevention by virtue of their antiproliferative effects. Prevention of malignancy after transplant through mTOR inhibitor-based immunosuppression may have a specific practical application in transplant recipients with preexisting malignancy including hepatocellular carcinoma or cholangiocarcinoma. This review will reveal how the biochemistry of the mTOR pathway, as it pertains to chemoprevention, can support a clinical role for mTOR inhibitors in the prevention of malignancies, recurrent or de novo, after solid organ transplantation in selected patients.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.048
GPT teacher head0.392
Teacher spread0.343 · 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

Citations14
Published2015
Admission routes2
Has abstractyes

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