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Prognosis of acute kidney injury requiring renal replacement therapy in solid organ transplanted patients

2009· article· en· W2092522660 on OpenAlexaff
Emmanuel Charbonney, Patrick Saudan, Pierre-Alain Triverio, Kieran L. Quinn, Gilles Mentha, Pierre‐Yves Martin

Bibliographic record

VenueTransplant International · 2009
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineRenal replacement therapyAcute kidney injurySepsisInternal medicinePopulationEpidemiologyUnivariate analysisNephrologyKidney transplantationTransplantationIntensive care medicineMultivariate analysis

Abstract

fetched live from OpenAlex

Solid organ transplanted patients represent a complex and multi-morbid population with potential acute illness. They are at high risk not only for chronic renal failure (CRF), but also for acute kidney injury (AKI) and little is known about the overall epidemiology or prognosis. We conducted a retrospective review of all solid organ transplant patients who required emergency renal replacement therapy (RRT) for AKI during a period of 7.5 years. We identified 53 episodes of AKI requiring RRT occurring in 51 transplanted patients, and 58.5% of them were freshly (<48 h) transplanted when admitted in ICU. The majority of episodes were a result of cardio-circulatory or septic events (84%), and a large proportion of the AKI episodes were a result of multifactorial causes (27%). Overall 90 days mortality was 49%, and no difference was detected between kidney and nonkidney transplants. On univariate analysis, the risk factors for death were smoking status [OR = 4.09 (CI 95%: 1.16-14.43); P = 0.028] and sepsis [OR = 4.90 (CI 95%: 1.39-17.31); P = 0.014]. Transplanted patients with AKI are younger, more prone to be diabetic and to have previous chronic renal failure compared with the general ICU population, possibly in part because of their immunosuppressive therapy. Nevertheless, they have the same prognosis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.333
Teacher spread0.313 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations21
Published2009
Admission routes1
Has abstractyes

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