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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 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)0.233
metaresearch head score (Gemma)0.475
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2330.475
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0130.034
Bibliometrics0.0130.007
Science and technology studies0.0010.003
Scholarly communication0.0060.004
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0120.001

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 head0.532
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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