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Record W4403123643 · doi:10.26685/urncst.645

The Effect of Existing Heart Allocation Criteria on Transplant Outcomes Globally: A Systematic Review

2024· review· en· W4403123643 on OpenAlexaff
Rachel Serrao, Sumaiya Iqbal, D.M.D.C. Coutinho

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2024
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIntensive care medicineSystematic reviewMedicineComputer scienceMEDLINEPolitical scienceLaw

Abstract

fetched live from OpenAlex

Introduction: Adult and pediatric heart allocation systems worldwide categorize transplant patients based on diverse criteria that impact mortality rates and quality of life. However, there is limited research examining the effectiveness of these systems. This study aims to address this gap by comprehensively comparing different adult heart allocation systems and a pediatric allocation system to identify potential challenges and provide valuable insights for optimizing heart transplant allocation strategies. Methods: The review was conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions. The protocol was registered in PROSPERO (CRD42024513009). An Ovid-MEDLINE and Ovid-Healthstar database search was conducted from January 1, 2024 to January 21, 2024 with relevant search terms. Articles were selected if they used quantitative or qualitative data, were published in the English language, described defined allocation frameworks specific to cardiac surgeries, data was retrieved from hospital-based interventions, and were peer-reviewed. Reviewers screened all articles using the COVIDENCE tool with vetted articles undergoing full-text extraction. The JBI Critical Appraisal tool for systematic reviews was used for risk of bias assessment. A thematic analysis was conducted with a qualitative analysis of intervention effectiveness. The robvis tool assessed the risk of reporting bias. Results: The database search yielded 630 unique articles. Following screening, 15 articles were selected for analysis. The selected articles described four countries’ national allocation policies; the United Kingdom (n=1), Switzerland (n=1) France (n=2), and the United States of America (n=11). The articles, published between 2016 and 2024, focused on comparing patient outcomes and waitlist times before and after national allocation policy changes. Discussion: Five articles found improvement in patient outcomes, six articles reported improvement in patient mortality, and six articles found a reduction in waiting time following policy change. The review identifies mixed results regarding the efficacy of various heart allocation frameworks. Conclusion: The study emphasizes a requirement for further research due to limited access to relevant articles. Additionally, global heart allocation networks are urged to report patient outcomes to allow for a broader, comprehensive analysis of framework efficacy, thereby allowing for a successful informing of policies.

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)0.025
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.112
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.009
Bibliometrics0.0100.013
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.173
GPT teacher head0.566
Teacher spread0.393 · 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 designSystematic review
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

Citations0
Published2024
Admission routes1
Has abstractyes

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