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Record W2016442280 · doi:10.1097/hco.0b013e32802bf772

New advances in antirejection therapy

2007· review· en· W2016442280 on OpenAlexaff
Michael Chan, Glen J. Pearson

Bibliographic record

VenueCurrent Opinion in Cardiology · 2007
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineIntensive care medicineAzathioprineHeart transplantationTacrolimusRituximabTransplantationImmunologySurgeryInternal medicineDisease

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The prevention and treatment of rejection have been the major focus of clinical and research studies since the inception of heart transplantation. Recent improvement in survival after transplant has been in large part due to continued advancement in antirejection therapies. RECENT FINDINGS: The combination of steroids/cyclosporine/azathioprine has been widely used since the early 1980s. The last decade has seen the increasing use of the drugs mycophenolate mofetil and tacrolimus. Newer agents such as target of rapamycin protein inhibitors and anti-interleukin-2 inhibitors have come under intense research recently, and may play a significant role in heart transplantation. Further study is required for agents such as rituximab. With the recent introduction of a new grading of cardiac allograft rejection, controversy remains over when rejection should be treated and which agents should be used. SUMMARY: Use of newer proven antirejection drugs has reduced rejection and improved survival after heart transplantation. Rejection and side effects from these drugs are still major problems, however; therefore continued research in this area is required.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.268
GPT teacher head0.541
Teacher spread0.272 · 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 designOther design
Domainnot available
GenreReview

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
Published2007
Admission routes1
Has abstractyes

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