Association between randomised trial evidence and global burden of disease: cross sectional study (Epidemiological Study of Randomized Trials--ESORT)
Bibliographic record
Abstract
OBJECTIVES: To determine whether an association exists between the number of published randomised controlled trials and the global burden of disease, whether certain diseases are under-investigated relative to their burden, and whether the relation between the output of randomised trials and global burden of disease can be explained by the relative disease burden in high and low income regions. DESIGN: Cross sectional investigation. STUDY SAMPLE: All primary reports of randomised trials published in December 2012 and indexed in PubMed by 17 November 2013. MAIN OUTCOME MEASURES: Number of trials conducted and number of participants randomised for each of 239 different diseases or injuries; variation in each outcome explainable by total disability adjusted life years (a measure of the overall burden of each disease) and the ratio of disability adjusted life years in low income to high income regions (a measure of whether a disease is more likely to affect people living in high income regions) quantified using multivariable regression. RESULTS: 4190 abstracts were reviewed and 1351 primary randomised trials identified, of which 1097 could be classified using the global burden of disease taxonomy. Total disability adjusted life years was poorly associated with number of randomised trials and number of participants randomised in univariable analysis (Spearman's r=0.35 and 0.33, respectively), although it was a significant predictor in the univariable and multivariable models (P<0.001). Diseases for which the burden was predominantly located in low income regions had sevenfold fewer trials per million disability adjusted life years than diseases predominantly located in high income regions. However, only 26% of the variation in number of trials among diseases could be explained by total disability adjusted life years and the ratio of disability adjusted life years in low income regions to high income regions. Many high income type diseases (for example, neck pain, glomerulonephritis) have proportionally fewer randomised trials compared with low income type diseases (for example, vitamin A deficiency). CONCLUSIONS: Overall, a weak association existed between global burden of disease and number of published randomised trials. A global observatory for research is needed to monitor and reduce the discordance between the output of randomised trials and global burden of disease.
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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 | 0.315 | 0.627 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".