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Record W2806874266 · doi:10.1111/petr.13190

Comparison of basiliximab vs antithymocyte globulin for induction in pediatric heart transplant recipients: An analysis of the International Society for Heart and Lung Transplantation database

2018· article· en· W2806874266 on OpenAlexaff
Ryan J. Butts, Anne I. Dipchand, David Sutcliffe, Maria Bano, V. Vivian Dimas, Robert Morrow, Bibhuti B. Das, Richard Kirk

Bibliographic record

VenuePediatric Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsBasiliximabMedicineCohortInternal medicineHeart transplantationLung transplantationTransplantationSurgeryGastroenterologyKidney transplantation

Abstract

fetched live from OpenAlex

This study aims to compare 2 common induction strategies, basiliximab and ATG. Analysis of the ISHLT transplant registry was performed. The database was queried for pediatric heart transplants from January 1, 2000, to June 30, 2015, who had received induction with basiliximab or ATG. Primary end-point was graft survival. Secondary end-points included 1-year survival and 1-year conditional survival. There were 3158 heart transplants who received induction with basiliximab or ATG. The ATG cohort was younger, more likely to have congenital heart disease or be a retransplant, have a higher PRA, longer ischemic time, and been transplanted earlier in the study period (all P<.01). There was no difference in graft loss in the basiliximab cohort compared to the ATG cohort (HR 1.18 P=.06). On conditional 1-year survival analysis, basiliximab induction was associated with graft loss (HR=1.35 95% CI 1.1-1.7, P<.01), and in the propensity-matched cohort, the basiliximab cohort was more likely to experience rejection prior to discharge (P=.04). Infection prior to discharge was more common in the antithymocyte cohort. Induction with ATG is associated with improved late graft survival compared to basiliximab.

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)0.001
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 score0.036
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.374
Teacher spread0.335 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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