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The critical pathway for deceased donation: reportable uniformity in the approach to deceased donation

2011· review· en· W2128287359 on OpenAlexaff
Beatriz Domínguez‐Gil, Francis L. Delmonico, F A Shaheen, R. Matesanz, Kevin O’Connor, М. Г. Минина, Elmi Muller, Kimberly Young, Martí Manyalich, Jeremy R. Chapman, Günter Kirste, Leen Coene, Valter Duro Garcı́a, Serguei Gautier, Tomonori Hasegawa, Vivekanand Jha, Tong Kiat Kwek, Zhonghua Klaus Chen, B. Loty, Alessandro Nanni Costa, Howard M. Nathan, Rutger J. Ploeg, О. Н. Резник, J.D. Rosendale, Annika Tibell, George Tsoulfas, Anantharaman Vathsala, Luc Noël

Bibliographic record

VenueTransplant International · 2011
Typereview
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsCanadian Blood Services
Fundersnot available
KeywordsMedicineDonationOrgan donationReferralIntensive care medicineFamily medicineTransplantationSurgeryLaw

Abstract

fetched live from OpenAlex

The critical pathway of deceased donation provides a systematic approach to the organ donation process, considering both donation after cardiac death than donation after brain death. The pathway provides a tool for assessing the potential of deceased donation and for the prospective identification and referral of possible deceased donors.

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)0.028
metaresearch head score (Gemma)0.071
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 score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.071
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.091
GPT teacher head0.360
Teacher spread0.269 · 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

Citations231
Published2011
Admission routes1
Has abstractyes

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