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Record W2804933123 · doi:10.1111/ajt.14946

Transplantation of pediatric renal allografts from donors less than 10 kg

2018· article· en· W2804933123 on OpenAlexaff
Nicholas Mitrou, Shahid Aquil, Marie Dion, Vivian C. McAlister, Alp Şener, Patrick Luke

Bibliographic record

VenueAmerican Journal of Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineTransplantationCreatinineSurgeryComplicationRenal transplantThrombosisBody weightUrologyInternal medicine

Abstract

fetched live from OpenAlex

Few transplant programs use kidneys from donors with body weight (BW) < 10 kg. We hypothesized that pediatric en bloc transplants from donors with BW < 10 kg would provide similar transplant outcomes to larger grafts. All pediatric en bloc renal transplants performed at our center between 2001 and 2017 were reviewed (N = 28). Data were stratified by smaller (donor BW < 10 kg; n=11) or larger donors (BW > 10 kg; n=17). Renal volume was assessed during follow-up with ultrasound. Demographic characteristics were similar between the 2 groups of recipients. After mean follow-up of 44 months (smaller donors) and 124 months (larger donors), graft and patient outcomes were similar between groups. Serum creatinine at 1, 3, and 5 years was no different between groups. At 1 day posttransplant, mean total renal volume in the smaller donors was 28 ± 9 mm 3 vs 45 ± 12 mm 3 ( P < .01). By 3 weeks, it was 53 ± 19 mm 3 (smaller donors) versus 73 ± 19 mm 3 (larger donors) ( P = NS). Complication rates were similar between both groups with 1 case of venous thrombosis in the smaller group. With experience, outcomes are equivalent to those from larger pediatric donors.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.018
GPT teacher head0.287
Teacher spread0.269 · 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 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

Citations25
Published2018
Admission routes1
Has abstractyes

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