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Record W3107801182 · doi:10.1111/vox.13037

Vox Sanguinis International Forum on Mitigation Strategies to Prevent Faint and Pre‐faint Adverse Reactions in Whole Blood Donors: Summary

2020· article· en· W3107801182 on OpenAlexaff
Mindy Goldman, Mary Townsend, Karin Magnussen, Miquel Lozano, Lise Sofie Haug Nissen‐Meyer, Cheuk Kwong Lee, Jennifer N. S. Leung, Minoko Takanashi, Jennifer McKay, Maria Kvist, Nancy Robitaille, Jessyka Deschênes, Emanuele Di Angelantonio, Amy McMahon, David J. Roberts, Mahtab Maghsudlu, Johanna Castrén, Pierre Tiberghien, Geneviève Woimant, Pascal Morel, Harry Kamel, Marjorie D. Bravo, Eilat Shinhar, Veronica Gendelman, Hana Raz, Silvano Wendel, Roberta Fachini, Franke A. Quee, Katja van den Hurk, Jo Wiersum, Kathleen M. Grima, Joanna Speedy, Mie Topholm Bruun, Nancy M. Dunbar

Bibliographic record

VenueVox Sanguinis · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsCanadian Blood Services
Fundersnot available
KeywordsLibrary scienceInterlibrary loanOperations researchComputer scienceEngineering

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.015
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0150.003

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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
GenreCommentary

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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