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Record W6983749278

Novel Bayesian approaches for the planning and monitoring phases of clinical studies

2023· dissertation· en· W6983749278 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2023
Typedissertation
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsnot available
FundersJanssen PharmaceuticalsSanofi PasteurMassachusetts Institute of TechnologyMassachusetts General HospitalSanofiNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaBill and Melinda Gates Foundation
KeywordsBayesian probabilityBayesian inferenceFeature (linguistics)Bayes estimatorBayesian network
DOInot available

Abstract

fetched live from OpenAlex

3-5, and a conclusion which includes a comprehensive discussion of the findings and implications of the thesis.The introduction, literature review, and conclusion contained in Chapters 1, 2, and 6 were written by Armando Turchetta

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 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)0.045
metaresearch head score (Gemma)0.181
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.955
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.181
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.004
Science and technology studies0.0020.003
Scholarly communication0.0050.007
Open science0.0040.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.769
GPT teacher head0.582
Teacher spread0.187 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations0
Published2023
Admission routes1
Has abstractyes

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