The role of study quality in aspartame and cancer epidemiology study reviews
Bibliographic record
Abstract
Toews et al. [1] and the World Health Organization (WHO) [2] reviewed observational epidemiology studies of non-sugar sweeteners (NSSs) and various health effects. The former used the Risk of Bias in Non-randomised Studies - of Interventions (ROBINS-I) tool and the latter used both the ROBINS-I tool and the Newcastle-Ottawa Scale to evaluate study quality. Both reviews concluded that there were no associations between NSS or aspartame consumption and cancer (except possibly between saccharin and bladder cancer) but indicated that the certainty of the evidence for all cancer types was "very low." While we agree with this conclusion, the support for the confidence in the evidence generally was not transparently documented, as the results of the study quality assessment were only provided in scores or ratings. An examination of illustrative case studies shows that some important aspects of study quality domains specific for NSSs generally or aspartame specifically (i.e., issues with the exposure and outcome assessments, the consideration of confounding/covariates, and selection bias) may have been overlooked or not given appropriate consideration, while other aspects that were less likely to have a large impact on overall study quality dominated the results in the two assessments. Our review of other studies published after the WHO [2] review further demonstrates this point. While this may not seem important given the overall lack of associations, it impacts the degree to which evidence supports a lack of effects as opposed to not being adequate to evaluate associations. In the future, aspartame and cancer outcome reviews should focus on those study quality domains that are most likely to impact the interpretation of results and discuss them in a transparent, systematic manner. If there is very low certainty in the evidence as a result of low study quality, reviewers should conclude the evidence is inadequate for making a causal determination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.640 | 0.890 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.021 | 0.027 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".