MétaCan
Menu
Back to cohort

Pediatric Coronary Allograft Vasculopathy-A Review of Pathogenesis and Risk Factors

2011· review· en· W1945558302 on OpenAlexaff
Kurt R. Schumacher, Robert J. Gajarski, Simon Urschel

Bibliographic record

VenueCongenital Heart Disease · 2011
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineContext (archaeology)DiseasePathogenesisCardiac allograft vasculopathyIntensive care medicineTransplantationHeart transplantationRisk factorImmune systemCardiologyInternal medicineImmunology

Abstract

fetched live from OpenAlex

Coronary allograft vasculopathy is the current leading cause for late graft loss following cardiac transplantation. Its pathogenesis is multifactorial, including immune, constitutional and genetic factors, metabolism, infection, as well as potential injury from routine immunosuppressive therapy. Children represent a patient group with unique differences: their pretransplant history rarely includes ischemic heart disease and risk factors for atherosclerotic heart disease, but many are presensitized from use of allograft material during reconstructive cardiac surgeries. Compared with older children and adults, infants and young children show significantly lower rates of graft vasculopathy that may be related to the relative immaturity of their immune system. This review summarizes the current concepts of coronary allograft vasculopathy derived mainly from animal models and adult clinical observations. It provides an overview of confirmed risk factors and explains their interactions. The characteristics and unique clinical findings among pediatric transplant recipients will be explored within the context of recent, albeit limited, scientific investigations.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.056
GPT teacher head0.344
Teacher spread0.288 · 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 designNot applicable
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

Citations35
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCongenital Heart DiseaseSame topicTransplantation: Methods and OutcomesFrench-language works237,207