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Record W2153904763 · doi:10.1681/asn.2009040412

Delayed Graft Function and the Risk for Death with a Functioning Graft

2009· article· en· W2153904763 on OpenAlexafffund
Shruti Tapiawala, Kathryn Tinckam, Carl J. Cardella, Jeffrey Schiff, Daniel Cattran, Edward Cole, S. Joseph Kim

Bibliographic record

VenueJournal of the American Society of Nephrology · 2009
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of TorontoToronto General HospitalUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsMedicineHazard ratioConfidence intervalDialysisRelative riskProportional hazards modelKidney transplantationInternal medicineTransplantationSurgeryCohortUrology

Abstract

fetched live from OpenAlex

Delayed graft function (DGF) associates with an increased risk for graft failure, but its link with death with graft function (DWGF) is unknown. We used the US Renal Data System to assemble a cohort of all first, adult, deceased-donor kidney transplant recipients from January 1, 1998, through December 31, 2004. In total, 11,542 (23%) of 50,246 recipients required at least one dialysis session in the first week after transplantation. Compared with patients without DGF, patients with DGF were significantly more likely to die with a functioning graft (relative hazard 1.83 [95% confidence interval 1.73 to 1.93] and 1.53 [95% CI 1.45 to 1.63] for unadjusted and fully adjusted models, respectively). The risk for DWGF was slightly higher among women with DGF than among men. There was no significant heterogeneity among other subgroups, and the results were robust to sensitivity analyses. Acute rejection within the first year attenuated the DGF-DWGF association. Cardiovascular and infectious deaths were slightly more prevalent in the DGF group, but the relative hazards of cause-specific death were similar between DWGF and deaths during total follow-up. In summary, DGF associates with an increased risk for DWGF; the mechanisms underlying the negative impact of DGF require further study.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.147

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)0.0000.000

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 head0.013
GPT teacher head0.270
Teacher spread0.258 · 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 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

Citations225
Published2009
Admission routes2
Has abstractyes

Explore more

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