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Record W4323531009 · doi:10.11124/jbies-22-00434

From critical appraisal to risk of bias assessment: clarifying the terminology for study evaluation in JBI systematic reviews

2023· article· en· W4323531009 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJBI Evidence Synthesis · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsQueen's University
Fundersnot available
KeywordsCritical appraisalOperationalizationTerminologySystematic reviewPsychologyEvidence-based medicineEvidence-based practiceHealth careMEDLINEManagement scienceApplied psychologyMedical educationMedicineAlternative medicinePolitical scienceEpistemologyEngineering

Abstract

fetched live from OpenAlex

The foundations for critical appraisal of literature have largely progressed through the development of epidemiologic research methods and the use of research to inform medical teaching and practice. This practical application of research is referred to as evidence-based medicine and has delivered a standard for the health care profession where clinicians are equally as engaged in conducting scientific research as they are in the practice of delivering treatments. Evidence-based medicine, now referred to as evidence-based health care, has generally been operationalized through empirically supported treatments, whereby the choice of treatments is substantiated by scientific support, usually by means of an evidence synthesis. As evidence synthesis methodology has advanced, guidance for the critical appraisal of primary research has emphasized a distinction from the assessment of internal validity required for synthesized research. This assessment is conceptualized and branded in various ways in the literature, such as risk of bias, critical appraisal, study validity, methodological quality, and methodological limitations. This paper provides a discussion of the definitions and characteristics of these terms, concluding with a recommendation for JBI to adopt the term "risk of bias" assessment.

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.

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)0.449
metaresearch head score (Gemma)0.872
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4490.872
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0030.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.765
GPT teacher head0.620
Teacher spread0.145 · 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