Challenges in reproducing results from publicly available data: an example of sexual orientation and cardiovascular disease risk
Bibliographic record
Abstract
BACKGROUND: Replication is a vital part of the research process and has recently received considerable attention. Analyses using publicly available data should, if adequately described, be reproducible without assistance from the original investigators. Using data from the US National Health and Nutrition Examination Survey (NHANES), a recent study reported a statistically significant difference in cardiovascular disease risk comparing subgroups of sexual minority men. We attempted to reproduce these findings and assessed whether the results were robust to alternative analytic strategies and assumptions. METHODS: We used the exclusion criteria and coding strategy described in the original paper to construct our analytical data set. Sampling weights were constructed in accordance with NHANES analytical guidelines. We estimated crude and covariate-adjusted associations between sexual orientation and vascular age using the regression models specified in the original report. We also conducted a series of sensitivity analyses to improve on the original findings. RESULTS: Our replication attempt was partially successful: we replicated the general trends reported in the original analysis, but not identical effect estimates. Importantly, we identified a potential misapplication of the Framingham Risk Score; correcting for this increased the probability that the reported null hypothesis test was a type I error. CONCLUSIONS: This paper supports the recent calls for greater transparency and improved reporting in research. Even with a publicly available and well-documented data source, we were unable to exactly replicate another study's original findings. Our sensitivity analyses revealed key issues in the original analysis and demonstrate the scientific importance of research replication.
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How this classification was reachedexpand
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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.788 | 0.914 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".