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Record W3043725192 · doi:10.1038/s41416-020-0990-8

Increasing operational and scientific efficiency in clinical trials

2020· article· en· W3043725192 on OpenAlexaff
Deirdre Kelly, Anna Spreafico, Lillian L. Siu

Bibliographic record

VenueBritish Journal of Cancer · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsClinical trialDrug developmentKey (lock)MedicineMEDLINEIntensive care medicineMedical physicsComputer scienceDrugPathologyPharmacologyPolitical science

Abstract

fetched live from OpenAlex

Operational and scientific inefficiencies in clinical trials represent roadblocks that need to be identified and circumvented to advance drug development in oncology. The collaboration of key stakeholders to advance this agenda is crucial to accelerate clinical research and ultimately benefit patient care through the optimal allocation of time and resources.

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)0.468
metaresearch head score (Gemma)0.725
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.532
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4680.725
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0080.007
Science and technology studies0.0020.019
Scholarly communication0.0220.025
Open science0.0060.015
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0100.004

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 head0.231
GPT teacher head0.422
Teacher spread0.191 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
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

Citations23
Published2020
Admission routes1
Has abstractyes

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