A Dynamic Model of Etiology in Sport Injury: The Recursive Nature of Risk and Causation
Bibliographic record
Abstract
The purpose of this manuscript is to outline a new model representing a dynamic approach that incorporates the consequences of repeated participation in sport, both with and without injury. This model builds on the previous work, while emphasizing the fact that adaptations occur within the context of sport (both in the presence and absence of injury) that alter risk and affect etiology in a dynamic, recursive fashion. Regardless of the type of injury, it is often preceded by a chain of shifting circumstances that, when they come together, constitute sufficient cause to result in an injury. If we are to truly understand the etiology of injury and target appropriate prevention strategies, we must look beyond the initial set of risk factors that are thought to precede an injury and take into consideration how those risk factors may have changed through preceding cycles of participation, whether associated with prior injury or not. This model considers the implications of repeated exposure, whether such exposure produces adaptation, maladaptation, injury or complete/incomplete recovery from injury. When feasible, future studies on sport injury prevention should adopt a methodology and analysis strategy that takes the cyclic nature of changing risk factors into account to create a dynamic, recursive picture of etiology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".