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Record W4224249897 · doi:10.23937/2572-4061.1510044

Meta-Analyses of Glyphosate and Non-Hodgkin's Lymphoma: Expert Panel Conclusions and Recommendations

2022· article· en· W4224249897 on OpenAlexaff
CR Kirman, P Cocco, GD Eslick, PJ Villeneuve, SM Hays

Bibliographic record

VenueJournal of Toxicology and Risk Assessment · 2022
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsCarleton University
Fundersnot available
KeywordsConfidence intervalTransparency (behavior)Meta-analysisDelphi methodDelphiMedicineActuarial sciencePsychologyComputer scienceInternal medicineArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

An expert panel was assembled to support a review of a series of recent publications using a modified Delphi format. These publications were scored based on a consideration of confidence in their methods, results, conclusions, and applicability to risk-based decision making. Mean confidence scores for the papers reviewed ranged from 53 to 74 (maximum score = 100), and key strengths and concerns were identified. This review highlights the need for transparency in meta-analyses.

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 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)0.191
metaresearch head score (Gemma)0.318
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.191
Threshold uncertainty score0.998

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1910.318
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.015
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0070.003
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0040.002

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 head0.109
GPT teacher head0.406
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

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