Ecological fallacy as a novel risk factor for poor translation in neuroscience research: A systematic review and simulation study
Bibliographic record
Abstract
BACKGROUND: Translational neuroscience is largely concerned with establishing causal links between biological processes and functional outcomes. Exciting new methods have emerged and top-tier biomedical journals are placing increasingly high demand for experiments that link outcomes. One pitfall to making these connections is the "ecological fallacy"-establishing a relationship between outcomes based on aggregate (averaged) results (a distinct issue from correlation vs causation). METHODS: To showcase the ecological fallacy, we first used simulated data to define and demonstrate the problem. Next, we performed a systematic review to determine the prevalence of the fallacy in top-tier biomedical journals (Science, Nature Medicine, Neuron, Nature, Nature Neuroscience, Cell). Based on our own research interests and specializations, we specifically focused on recent publications in the area of spinal cord injury and regenerative medicine. RESULTS: Of the articles reviewed which examined a relationship between central nervous system regeneration and a behavioural outcome, 100% (21/21) were subject to possible ecological fallacy. CONCLUSIONS: Ecological fallacy is highly prevalent in neuroscience research and could partially account for translation failures in this field. Reporting guidelines for in vivo experiments should include subject-level correlation analyses for the primary outcomes.
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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 | 0.199 | 0.438 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".