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Record W3011217463 · doi:10.1111/petr.13679

Pediatric donor management to optimize donor heart utilization

2020· review· en· W3011217463 on OpenAlexaff
Daniel Zimpfer, Peta Alexander, Ryan R. Davies, Anne I. Dipchand, Brian Feingold, Anna Joong, Karen Lord, Richard Kirk, Angie Scales, Renata Shih, Oliver Miera

Bibliographic record

VenuePediatric Transplantation · 2020
Typereview
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineIntensive care medicineHormone replacementHormonePediatricsInternal medicine

Abstract

fetched live from OpenAlex

Optimal management of pediatric cardiac donors is essential in order to maximize donor heart utilization and minimize the rate of discarded organs. This review was performed after a systematic literature review and gives a detailed overview on current practices and guidelines. The review focuses on optimal monitoring of pediatric donors, donor workup, hormonal replacement, and obliterating the adverse effects of brain death. The current evidence on catecholamine support and thyroid hormone replacement is also discussed. Recognizing and addressing this shall help in a standardized approach toward donor management and optimal utilization of pediatric heart donors organs.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.057
GPT teacher head0.336
Teacher spread0.279 · 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.

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

Citations3
Published2020
Admission routes1
Has abstractyes

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