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Record W3097659958 · doi:10.1111/petr.13884

Post‐transplant malignancies in pediatric organ transplant recipients

2020· article· en· W3097659958 on OpenAlexaff
Cal Robinson, Carrie C. Coughlin, Rahul Chanchlani, Vikas R. Dharnidharka

Bibliographic record

VenuePediatric Transplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsImpactMcMaster Children's HospitalMcMaster Divinity CollegeMcMaster University
Fundersnot available
KeywordsMedicineImmunosuppressionCancerOrgan transplantationTransplantationPopulationSkin cancerPediatric cancerDiseaseOncologyIntensive care medicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

The majority of cancer diagnoses in pediatric solid organ transplant recipients (SOTRs) are post-transplantation lymphoproliferative disorders (PTLD) or skin cancers. However, pediatric SOTRs are also at significantly elevated risk for multiple other solid and hematological cancers. The risks of specific cancers vary by transplanted organ, underlying disease, and immunosuppression factors. More than one-quarter of pediatric SOTRs develop cancer within 30 years of transplantation and their risk of solid cancer is 14 times greater than the general population. Pediatric SOTRs are at significantly higher risk of cancer-associated death. Improving patient survival among pediatric SOTRs puts them at risk of adult epithelial cancers associated with environmental carcinogenic exposures. Vaccination against oncogenic viruses and avoidance of excessive immunosuppression may reduce the risk of solid cancers following transplantation. Patient and family education regarding photoprotection is an essential component of skin cancer prevention. There is significant variability in cancer screening recommendations for SOTRs and general population approaches are typically not validated for transplant populations. An individualized approach to cancer screening should be developed based on estimated cancer risk, patient life expectancy, and screening test performance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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 teacher head, not a consensus.

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

Citations20
Published2020
Admission routes1
Has abstractyes

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