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Clinical trials evaluating pathogen‐reduced platelet products: methodologic issues and recommendations

2012· article· en· W1829876109 on OpenAlexaff
Richard J. Cook, Nancy M. Heddle

Bibliographic record

VenueTransfusion · 2012
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsHamilton Health SciencesMcMaster UniversityCanadian Blood ServicesUniversity of Waterloo
Fundersnot available
KeywordsClinical trialRandomized controlled trialResearch designClinical study designMedicineRelevance (law)Intensive care medicineMEDLINEBiologySurgeryInternal medicineStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Several randomized trials of platelet (PLT) products have been conducted with different study designs, endpoints, and analyses. The purpose of this article is to discuss methodologic issues in the design and analysis of PLT transfusion trials evaluating pathogen reduction technology and make recommendations for the conduct of future trials. STUDY DESIGN AND METHODS: Six randomized clinical trials of pathogen-inactivated PLT products are reviewed and associated methodologic issues are discussed. RESULTS: The variation in the trial designs, outcomes, and methods of analysis suggest the need to harmonize the way trials of pathogen-reduced PLT products are conducted to facilitate comparisons between studies and the synthesis of results. Recommendations are made with this goal in mind and to increase the rigor and relevance of findings from future trials. CONCLUSIONS: Future randomized trials of pathogen-reduced PLT products should be based on a clearly stated hypothesis driven by an important research question, a design that is optimal for the research question, outcomes that relate to the research question, clearly defined observation periods, and statistical analyses that lead to valid tests of these hypotheses and associated estimates of treatment effect.

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)0.018
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.002
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)0.0010.000

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 head0.441
GPT teacher head0.534
Teacher spread0.093 · 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 designOther design
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

Citations13
Published2012
Admission routes1
Has abstractyes

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