Applying Bradford Hill's Criteria for Causation to Neuropsychiatry
Bibliographic record
Abstract
Establishing an argument of causation is an important research activity with major clinical and scientific implications. Sir Austin Bradford Hill proposed criteria to establish such an argument. These criteria include the strength of the association, consistency, specificity, temporal sequence, biological gradient, biologic rationale, coherence, experimental evidence, and analogous evidence. These criteria are reviewed with the goal of facilitating an increase in rigor for establishing arguments of causation in neuropsychiatry. The challenges and opportunities related to these criteria in neuropsychiatry are reviewed, as are two important arguments for causation: one for poststroke depression and one for brain injury as a cause of psychiatric disorders.
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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 | 0.095 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".