A Decision-Analytic Approach to Addressing the Evidence About Football and Chronic Traumatic Encephalopathy
Bibliographic record
Abstract
Abstract Doubts can be raised about almost any assertion that a particular exposure can lead to an increase in a given adverse health effect. Even some of the most well-accepted causal associations in public health, such as that linking cigarette smoking to increased lung cancer risk, have intriguing research questions remaining to be answered. The inquiry whether an exposure causes a disease is never wholly a yes/no question but ought to follow from an appraisal of the weight of evidence supporting the positive conclusion in light of any coherent theories casting doubt on this evidence and the data supporting these. More importantly, such an appraisal cannot be made sensibly without considering the relative consequences to public health and economic welfare of specific actions based on unwarranted credulity (false positives) versus unwarranted skepticism (false negatives). Here we appraise the weight of evidence for the premise that repeated head impacts (RHIs) in professional football can increase the incidence of chronic traumatic encephalopathy (CTE) and, in turn, cause a variety of cognitive and behavioral symptoms. We first dismiss four logical fallacies that should not affect the appraisal of the weight of evidence. We then examine four alternative hypotheses in which RHI is not associated with CTE or symptoms (or both), and we conclude that the chances are small that the RHI→ CTE→ symptoms link is coincidental or artifactual. In particular, we observe that there are many specific interventions for which, even under a skeptical appraisal of the weight of evidence, the costs of a false positive are smaller than the false negative costs of refusing to intervene.
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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 | 0.416 | 0.549 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".