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Record W2911422094 · doi:10.3389/fmed.2019.00035

Driving Medical Innovation Through Interdisciplinarity: Unique Opportunities and Challenges

2019· article· en· W2911422094 on OpenAlexafffundabout
Faekah Gohar, Patrick Maschmeyer, Bechara Mfarrej, Mathieu Lemaire, Lucy R. Wedderburn, Maria Grazia Roncarolo, Annet van Royen‐Kerkhof

Bibliographic record

VenueFrontiers in Medicine · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsHospital for Sick ChildrenSickKids FoundationUniversity of Toronto
FundersCanadian Institutes of Health ResearchVersus ArthritisMedical Research CouncilNational Institute for Health and Care ResearchCanadian Child Health Clinician Scientist Program
KeywordsFront (military)Translational medicinePolitical scienceEngineering ethicsData scienceMedicineComputer scienceEngineeringPathologyMechanical engineering

Abstract

fetched live from OpenAlex

OPINION article Front. Med., 26 February 2019Sec. Translational Medicine Volume 6 - 2019 | https://doi.org/10.3389/fmed.2019.00035

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)0.085
metaresearch head score (Gemma)0.096
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.448

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.096
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0080.014
Scholarly communication0.0320.032
Open science0.0060.029
Research integrity0.0160.017
Insufficient payload (model declined to judge)0.0290.009

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 head0.183
GPT teacher head0.424
Teacher spread0.240 · 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
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

Citations28
Published2019
Admission routes3
Has abstractyes

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