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Empirical evaluation of fundamental principles of evidence-based medicine: a meta-epidemiological study

2021· preprint· en· W4200365875 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

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGrading (engineering)Meta-analysisMedicineOdds ratioSystematic reviewPublication biasStatisticsMEDLINEEconometricsInternal medicineMathematicsChemistry

Abstract

fetched live from OpenAlex

Rationale, aims and objectives 39 Evidence-based medicine (EBM) holds that estimates of effects of health interventions based on 40 high-certainty evidence (CoE) are expected to change less frequently than the effects generated 41 in low CoE studies. However, this foundational principle of EBM has never been empirically 42 tested. 43 Methods 44 We reviewed all systematic reviews and meta-analyses in Cochrane Database of Systematic 45 Reviews from January 2016 through May 2021 (n=3,323). We identified 414(207x2) and 384 46 (192x2) pairs of original and updated Cochrane reviews that assessed CoE and pooled 47 treatment effect estimates. We appraised CoE using the Grading of Recommendations 48 Assessment, Development and Evaluation (GRADE) method. We assessed the difference in 49 effect sizes between the original versus updated reviews as a function of change in CoE, which 50 we report as a ratio of odds ratio (ROR). We compared ROR generated in the studies that 51 changed CoE from very low/low (VL/L) to moderate/high (M/H) vs. MH/H VL/L. We also 52 assessed the heterogeneity and inconsistency (using the tau and I2 statistic), the change in 53 precision of effect estimates (by calculating the ratio of standard errors) (seR), and the absolute 54 deviation in estimates of treatment effects (aROR). 55 Results 56 57 We found that CoE originally appraised as VL/L had 2.1 (95%CI: 1.19 to 4.12; p=0.0091) times 58 higher odds to be changed in the future studies than M/H CoE. However, the effect size was not 59 different when CoE changed from VL/L M/H vs. M/H VL/L [ROR=1.02 (95%CI: 0.74 to 1.39) 60 vs. 1.02 (95%CI: 0.44 to 2.37); p=1 for the between subgroup differences]. aROR was similar 61 between the subgroups [median (IQR):1.12 (1.07 to 1.57) vs 1.21 (1.12 to 2.43)]. We observed 62 large inconsistency (I 2=99%) and imprecision in treatment effects (seR=1.09). 63 Conclusions 64 We provide the first empirical support for a foundational principle of EBM showing that low65 quality evidence changes more often than high CoE. However, the effect size was not different 66 between studies with low vs high CoE. The finding that the effect size did not differ between low 67 and high CoE indicate urgent need to refine current EBM critical appraisal methods

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchMeta-epidemiology (broad)
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptMetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad)
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.615
metaresearch head score (Gemma)0.458
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6150.458
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0190.008
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1260.000

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 head0.992
GPT teacher head0.715
Teacher spread0.278 · 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