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Record W2787423031 · doi:10.1136/esmoopen-2017-000319

Extrapolation concept at work with biosimilar: a decade of experience in oncology

2018· article· en· W2787423031 on OpenAlexaboutno aff
Andriy Krendyukov, Martin Schiestl

Bibliographic record

VenueESMO Open · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsBiosimilarExtrapolationWork (physics)Medical physicsComputer scienceOncologyData scienceMedicineMedical educationInternal medicineMathematicsStatisticsEngineering

Abstract

fetched live from OpenAlex

Early in 2017, The European Society for Medical Oncology (ESMO) published a position paper on biosimilars for oncology prescribers.1 Since then, both the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have approved biosimilar versions of monoclonal antibodies (mAbs) used for the treatment of several cancer types (eg, rituximab in Europe, bevacizumab and trastuzumab in the USA).2 3 The ESMO position paper was rightly welcomed as a timely addition to the discussion around this important topic.

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)0.055
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0030.011
Scholarly communication0.0120.020
Open science0.0040.008
Research integrity0.0070.015
Insufficient payload (model declined to judge)0.0110.005

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 head0.066
GPT teacher head0.381
Teacher spread0.316 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations19
Published2018
Admission routes1
Has abstractyes

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