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Record W7088338878 · doi:10.1007/978-3-032-06112-6_1

Donors and Procurement

2025· book-chapter· en· W7088338878 on OpenAlexaff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Reproductive Biology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDonationOrgan procurementOrgan donationProcurementCold storageTransplantation

Abstract

fetched live from OpenAlex

Successful lung transplantation depends on careful donor selection and meticulous procurement, preservation, and transport. While donation after brain death (DBD) remains the standard, donation after circulatory death (DCD) has emerged as a viable alternative, with comparable results in experienced centers. Procurement protocols involving lung-protective ventilation, hormonal resuscitation, and judicious fluid management are critical to preserving graft function. Advances in organ preservation, particularly static cold storage at 10 °C, have extended safe ischemic times without compromising outcomes. Donor-specific factors, including age, smoking history, diabetes, and infection risk, continue to influence both graft viability and recipient survival.

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)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0630.063

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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