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Record W4365510493 · doi:10.1038/s41591-023-02294-8

Concerns about the Burden of Proof studies

2023· letter· en· W4365510493 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNature Medicine · 2023
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic, financial, and policy analysis
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchGovernment of CanadaFoundation for the National Institutes of Health
KeywordsBurden of proofProof of conceptMedicineComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

We read with interest the Burden of Proof (BoP) studies 1 , 2 , 3 , 4 , 5 in which the authors conducted meta-analyses of epidemiological studies to provide an overall conservative quantitative assessment for several important public health questions. For ease of interpretation, they transformed the overall assessment into a star rating (1–5 stars). Examples include five stars for smoking and lung cancer, two stars for low vegetable intake and ischemic heart disease (IHD) and two stars for unprocessed red meat and type 2 diabetes (T2D), colorectal cancer and IHD 6 . They used this same method to assign just three stars to smoking in relation to IHD and one or two stars to other well-established relationships 1 , 2 , 3 , 4 . However, we believe there are serious methodological issues with their meta-analyses 5 ; the star rating of evidence strength is overly simplistic and could cast doubt on existing recommendations and policies intended to prevent chronic disease and treat illnesses.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.081
GPT teacher head0.322
Teacher spread0.241 · 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