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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

between 32 and 35 L, 18-to 22-year-olds donate 450 ml rather than 485 ml No

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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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