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Record W2989326154 · doi:10.1111/petr.13614

Allograft renal cell carcinoma in pediatrics transplantation: A mini‐review

2019· review· en· W2989326154 on OpenAlexaff
Bita Geramizadeh, Pedram Keshavarz, Ali Kashkooe, Mahsa Marzban

Bibliographic record

VenuePediatric Transplantation · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineTransplantationRenal cell carcinomaCarcinomaIntensive care medicinePediatricsOncologyInternal medicine

Abstract

fetched live from OpenAlex

Renal cell carcinoma in the pediatric age group is a rare event, and its occurrence in the allograft (recipient) kidney is an uncommon event. There is no published review study in RCC of allograft kidneys in children and adolescents. In this study, we thoroughly searched English literature (PubMed, Google Scholar, and Google) in order to find all the reported allograft kidney RCCs in the patients who have been transplanted below the age of 18. There have been 12 reports of allograft RCC in this age group. Our result showed that the age of tumor detection according to donor age is lower comparing to non-allograft RCCs, and there is a significant male preponderance. RCC in the allografts is smaller and shows low nuclear grade and has a good prognosis. These findings emphasize the importance of routine allograft ultrasonography which results in earlier detection of RCC with smaller size and better outcome.

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)0.002
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 score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.005
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.273
Teacher spread0.257 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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