Understanding Causal Inference: The Future Direction in Sports Injury Prevention
Bibliographic record
Abstract
Although physical activity reduces mortality and morbidity, injuries associated with activity may increase both short- and long-term musculoskeletal disability. On the basis of basic science and injury epidemiology studies, authors have made conclusions about cause and effect (causal inferences) and have suggested various interventions to decrease the rate of injuries. However, recent advances in epidemiology suggest that the regression/stratification approach to adjustment for confounding does not provide an appropriate foundation for causal inference; therefore, hypotheses based on traditional analyses may be misleading. The purpose of this article is to provide an overview of the basic concepts of injury epidemiology related to causes, risk factors, and confounding, and to conceptually explain the more recent advances that allow for appropriate interpretations of cause and effect.
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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 | 0.167 | 0.274 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.014 | 0.045 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 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".