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Record W4410542100 · doi:10.1136/bmj-2024-083865

Core GRADE 5: rating certainty of evidence—assessing indirectness

2025· article· en· W4410542100 on OpenAlexaff
Gordon Guyatt, Alfonso Iorio, Hans de Beer, Andrew Owen, Thomas Agoritsas, M. Hassan Murad, Ganesan Karthikeyan, Carlos A. Cuello‐García, Manya Prasad, Kevin Kim, Dalal S. Ali, Arnav Agarwal, Lars G. Hemkens, Liang Yao, Monica Hultcrantz, Jamie Rylance, Derek K. Chu, Per Olav Vandvik, Benjamin Djulbegović, Reem A. Mustafa, Linan Zeng, Prashanti Eachempati, Bram Rochwerg, Kameshwar Prasad, Víctor M. Montori, Romina Brignardello‐Petersen

Bibliographic record

VenueBMJ · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsPopulation Health Research InstituteMcMaster UniversityImpact
Fundersnot available
KeywordsCertaintyCore (optical fiber)Computer scienceMedicineInformation retrievalPhilosophyEpistemologyTelecommunications

Abstract

fetched live from OpenAlex

This fifth article in a seven part series presents the Core GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach to systematic reviews, clinical practice guidelines, and health technology assessments and addresses issues of indirect evidence. Guideline developers and health technology assessment practitioners must carefully specify the population, intervention, comparison, and outcome (PICO)—their target PICO—and consider the extent to which the best available evidence matches their target. When target and study PICOs differ substantially, studies provide indirect evidence and Core GRADE users may rate down the certainty of evidence as a result of this indirectness. Whether examining studies from a search for direct evidence or a deliberate search for indirect evidence, for each substantial difference between target and study PICO Core GRADE users must judge the likelihood that magnitude of effects will differ substantially. The greater the likelihood of substantial differences the more advisable rating down for indirectness.

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.610
GPT teacher head0.611
Teacher spread0.001 · 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 teacher head, 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

Citations14
Published2025
Admission routes1
Has abstractyes

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