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Record W2911709863 · doi:10.1016/j.yrtph.2019.01.033

Evaluation of aspartame cancer epidemiology studies based on quality appraisal criteria

2019· review· en· W2911709863 on OpenAlexaff
Lois A. Haighton, Ashley Roberts, Tomas S. Jonaitis, Barry Lynch

Bibliographic record

VenueRegulatory Toxicology and Pharmacology · 2019
Typereview
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsAspartameMedicineEnvironmental healthProspective cohort studyEpidemiologyCohort studyFood scienceInternal medicineBiology

Abstract

fetched live from OpenAlex

Given the widespread use of the low-calorie sweetener aspartame over the last 30 years, the current work was undertaken to evaluate aspartame epidemiology studies looking at cancer endpoints against quality appraisal criteria. The quality appraisal tool used was from the National Heart, Lung and Blood Institute (NHLBI) of the National Institute of Health. Studies identified included nine case-control studies and five prospective cohort studies. Most studies assessed low-calorie or diet beverages rather than aspartame intake specifically; however, common use of aspartame in diet sodas does allow for some general extrapolation of results. Following consideration of study quality, two case-control and five prospective studies were considered to meet the majority of the NHLBI criteria. The primary limitation of the other case-control studies was an inadequate sample size. Overall, the results of the studies do not support that exposures to low and no-calorie sweeteners and beverages, and by extension aspartame, are associated with an increased risk of cancer in humans. • Aspartame epidemiology studies evaluated according to quality appraisal criteria. • Two case-control studies and five prospective studies were of reasonable quality. • Highest quality epidemiology studies do not support an increased risk of cancer.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.925
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.418
GPT teacher head0.589
Teacher spread0.170 · 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.

Study designNot applicable
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

Citations16
Published2019
Admission routes1
Has abstractyes

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