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Record W6889936707 · doi:10.3205/25ebm116

Biasrisiko in randomisierten, kontrollierten Studien bewerten – das RoB-2-Tool

2025· article· de· W6889936707 on OpenAlexaff

Bibliographic record

VenueGerman Medical Science (German Research Foundation) · 2025
Typearticle
Languagede
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsCochrane
Fundersnot available
KeywordsPopulationTerm (time)Context (archaeology)MEDLINERisk factor

Abstract

fetched live from OpenAlex

Beschreibung: Für eine informierte Gesundheitsentscheidung nach EbM-Kriterien sollte, neben der individuellen Fach-Expertise und den Wünschen der zu behandelnden Person, auch die bestverfügbare Evidenz aus relevanten Studien berücksichtigt werden. Um mögliche Verzerrungen (Bias) [zum vollständigen Text gelangen Sie über die oben angegebene URL]

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)0.547
metaresearch head score (Gemma)0.292
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.5470.292
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0070.029
Science and technology studies0.0030.007
Scholarly communication0.0070.002
Open science0.0160.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0490.047

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 head0.532
GPT teacher head0.610
Teacher spread0.078 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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