Surrogate endpoints in global health research: still searching for killer apps and silver bullets?
Bibliographic record
Abstract
In clinical research, there is widespread acceptance that surrogate endpoints may not translate to long-term benefits.1–3 Clinical epidemiologists highlight the hazards of surrogate measures (eg, biomarkers, laboratory test results and short-term improvements in health) that substitute for outcomes which are important for patients (eg, avoiding premature death or severe disability). For example, in cardiovascular research, improvements in parameters such as blood pressure or cholesterol may not improve outcomes such as deaths. Improvements in surrogate endpoints may not correlate with real outcomes of interest (and may even increase the risk of death, in some cases). And there are many examples and case studies in the literature that illustrate the hazards of using surrogates in clinical epidemiology.1–3 In comparison, in global health, we are often stunned when interventions that showed improvements in surrogate endpoints do not lead to lives being saved. Take, for example, the new tuberculosis (TB) detection technology, Xpert MTB/RIF(R) (Cepheid Inc, Sunnyvale, California, USA), an automated, molecular test for TB and drug resistance. Xpert MTB/RIF was first endorsed by WHO in 20104 and has since been rolled out in many countries with over 23 million tests conducted in the past 6 years.5 While the test is rapid, accurate and much superior to tests that have been in use for decades,6 some pragmatic randomised controlled trials (RCTs) did not show improvements in long-term outcomes such as reduction in mortality.7 8 These results have prompted media headlines such as ‘improved diagnostics fail to halt the rise of tuberculosis.”9 The recent RCT in India of the WHO Safe Childbirth Checklist presents another example. The WHO Safe Childbirth Checklist is a quality-improvement tool to promote systematic adherence to practices that have been associated with improved childbirth outcomes.10 In a large-scale study in 24 districts in India, adherence of birth attendants to …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".