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Record W2034646698 · doi:10.1159/000182217

Kidney Transplantation in Uremic Children with Cystinosis

2008· article· en· W2034646698 on OpenAlexaff
Robert Langlois, Sean O’Regan, Micheline Pelletier, Pierre Robitaille

Bibliographic record

Venue˜The œNephron journals/Nephron journals · 2008
Typearticle
Languageen
FieldMedicine
TopicBiomedical Research and Pathophysiology
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsCystinosisMedicineUremiaTransplantationKidney transplantationFulminantNephrologyHemodialysisKidneySurgeryPediatricsInternal medicineCystine

Abstract

fetched live from OpenAlex

10 children underwent cadaveric renal transplantation between the ages of 8.0 and 12.5 years for uremia secondary to infantile cystinosis. 6 children are doing well 6-62 months after-transplantation. 3 of the 4 other recipients required a second graft and eventually died of uremia or fulminant viral encephalitis, the other lost her first graft due to accelerated acute rejection and is now on maintenance hemodialysis. No further systemic complications of cystinosis have been observed in the patients with functioning grafts. Our experience confirms that kidney transplantation is the treatment of choice for uremic children with infantile cystinosis.

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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.307
Teacher spread0.274 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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