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Record W3021129457 · doi:10.1111/trf.15779

Revisiting study design and methodology for pathogen reduced platelet transfusions: a round table discussion

2020· review· en· W3021129457 on OpenAlexaff
Nancy M. Heddle, Márcia Cardoso, Pieter F. van der Meer

Bibliographic record

VenueTransfusion · 2020
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsMcMaster University
FundersTufts University School of MedicineDeutsches Rote KreuzUniversity of Texas Medical School at HoustonJohns Hopkins UniversityUniversity of WashingtonWashington University in St. LouisBeth Israel Deaconess Medical Center
KeywordsClinical trialMedicinePlateletIntensive care medicineClinical endpointPlatelet transfusionSurrogate endpointInternal medicine

Abstract

fetched live from OpenAlex

Pathogen inactivation/reduction technologies for platelet components have been developed to enhance microbial safety, and many studies have been carried out to determine whether this technique adversely affects the platelet's ability to stop or prevent bleeding. These clinical trials require inclusion of several hundred patients, are costly, and take many years to complete. To address these challenges, a meeting was organized consisting of two expert presentations followed by a roundtable discussion focused on possible new approaches to evaluate the clinical efficacy of pathogen-reduced platelets. The value of laboratory measures to provide information on platelet count after transfusion or to serve as a surrogate for bleeding risk was discussed. Also, other types of trial designs (cluster trials, stepped wedge designs, and Phase 4 postmarketing surveillance studies) as well as a clinically meaningful standardized safety endpoint to evaluate pathogen- reduced platelets were also discussed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.214
GPT teacher head0.401
Teacher spread0.188 · 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 designOther design
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

Citations6
Published2020
Admission routes1
Has abstractyes

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