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Record W2435045897 · doi:10.2450/2015.0130-15

Molecular immunohaematology round table discussions at the AABB Annual Meeting, Philadelphia 2014.

2016· other· en· W2435045897 on OpenAlexaff
Flegel Wa, De Castilho Sl, Keller Ma, Klapper Eb, Joann M. Moulds, F. Noizat‐Pirenne, Nadine Shehata, Gary Stack, Christopher A. Tormey, Waxman Da, Christof Weinstock, Silvano Wendel, Gregory A. Denomme

Bibliographic record

VenuePubMed · 2016
Typeother
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsHéma-QuébecMount Sinai Hospital
FundersNational Center for Advancing Translational Sciences
KeywordsGenotypingMedicineTransfusion medicineSerologyRound tableFamily medicineMedical educationImmunologyBlood transfusionGenotypeBiologyGeneticsAdvertising

Abstract

fetched live from OpenAlex

Use of molecular-based immunohaematology testing is becoming more widespread worldwide in laboratories that are accustomed to the use of blood group serology alone. Molecular immunohaematology issues may be challenging even for some established professionals in the field of blood group serology. At an international meeting, we offered round table discussions on four patient-related and two donor-related topics, which are current and possibly controversial. Six molecular immunohaematology questions were addressed: applications for highly contagious infections, such as Ebola; utility after transfusions in the preceding three months; root cause analysis for unexplained occurrence of anti-D; acceptable turnaround time for red cell genotyping of patients; criteria for donor cohorts to be genotyped; and quality assurance for discrepancies between serological phenotype and licensed red cell genotyping. The opinions polled in this workshop with an international assemblage of more than 100 transfusion medicine specialists were discussed in the light of education and training opportunities and the development of guidance in the field. We provide a summary report of the participants’ input to our questions and discuss the topics.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.031
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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
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

Citations10
Published2016
Admission routes1
Has abstractyes

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