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Record W2329611364 · doi:10.1097/nhh.0b013e31822a06e5

The Pediatric Renal Transplant Process

2011· review· en· W2329611364 on OpenAlexaff
Jennifer B. Williams, Stacie W. Bonin, Bela G. Emory, Ashley W. Tate, Gina Thompson, Susan M. Wood

Bibliographic record

VenueHome Healthcare Nurse · 2011
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsCARE Canada
Fundersnot available
KeywordsRenal transplantMedicineProcess (computing)Intensive care medicineComputer scienceInternal medicineTransplantationProgramming language

Abstract

fetched live from OpenAlex

Kidneys are one of the most commonly transplanted solid organs in children. In 2008, 16,067 renal transplants were performed in the United States; of those, 773 were performed on patients under the age of 18 (2009 OPTN/SRTR Annual Report 1999-2008, 2009). The process of renal transplantation can be a long one and children and their families often endure many challenges on the road to the transplant, not to mention the adjustments that lie ahead afterward. For this reason, and because these patients benefit from home health follow-up after their transplant, it is important for home health clinicians to be knowledgeable about the renal transplant process in addition to posttransplant care.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.077
GPT teacher head0.399
Teacher spread0.322 · 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 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

Citations0
Published2011
Admission routes1
Has abstractyes

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