Description, Prediction, and Causation in Sport and Exercise Medicine Research: Resolving the Confusion to Improve Research Quality and Patient Outcomes
Bibliographic record
Abstract
SYNOPSIS: Researchers often assign a label (such as a risk factor or predictor) to a characteristic that is statistically associated with an outcome (such as future injury). Labeling signifies that the characteristic has an established clinical value. More often than not, these labels are assigned prematurely and haphazardly. The rampant practice conflates research goals, the ultimate clinical value of the findings, and many risk factors/predictors that may not warrant the label. To address these issues and improve injury prevention research, we (1) outline the problem; (2) clarify the key differences between the research goals of description, causation, and prediction/prognosis (along with labeling conventions); (3) differentiate the clinical implications for each label; and (4) frame an appropriate scientific process to follow before applying a label. J Orthop Sports Phys Ther 2023;53(7):381–387. Epub: 26 April 2023. doi:10.2519/jospt.2023.11773
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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 | 0.230 | 0.448 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".