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Record W4250603225 · doi:10.1002/9780471420194.tnmm63

Cancer Following Solid Organ Transplant

2017· other· en· W4250603225 on OpenAlexaff
Jerome Laurence

Bibliographic record

VenueTNM Online · 2017
Typeother
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMalignancyMedicineCancerOrgan transplantationTransplantationDiseaseSolid organIncidence (geometry)EtiologyInternal medicineOncology

Abstract

fetched live from OpenAlex

Summary This chapter discusses the aetiology, incidence, screening, diagnosis, prognosis factors, and treatment of the cancer following solid organ transplant. After infection and cardiovascular disease, malignancy is the next most common cause of death in solid organ transplant recipients. Far more commonly, cancer develops de novo in the recipient some time after transplantation, with the risk increasing progressively as a function of time after transplantation. Overall, the risk of malignancy in transplant recipients is 200‐300% that of age‐ and sex‐matched controls. This increase in risk is mostly dependent upon the organ transplanted and the associated nature, intensity and duration of the immunosuppressive regimen. The magnitude of the increase in cancer risk varies by cancer type and site. The greatest increase is observed in cancers associated with oncogenic viral infections. The prognosis for each cancer is substantially inferior for transplant recipients than for patients who are not transplant recipients.

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)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.004

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 head0.018
GPT teacher head0.327
Teacher spread0.310 · 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
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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