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Record W3169566153 · doi:10.21203/rs.3.rs-603176/v1

Impact of industry sponsorship on the quality and interpretation of systematic reviews of vaccines: a cross-sectional analysis of studies published from 2016–2019

2021· preprint· en· W3169566153 on OpenAlexaff
Dawid Pieper, Irma Hellbrecht, Linlu Zhao, Clemens Baur, Georgia Pick, Sarah Schneider, Thomas Harder, Kelsey Young, Andrea C. Tricco, Ella Westhaver, Matthew Tunis

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsHealth Canada
Fundersnot available
KeywordsQuality (philosophy)Interpretation (philosophy)Computer scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.352
metaresearch head score (Gemma)0.687
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.799

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3520.687
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0140.020
Science and technology studies0.0010.002
Scholarly communication0.0090.006
Open science0.0020.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.886
GPT teacher head0.683
Teacher spread0.202 · 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 designObservational
DomainEvaluation
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

Citations1
Published2021
Admission routes1
Has abstractno

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