A higher sport-related reinjury risk does not mean inadequate rehabilitation: the methodological challenge of choosing the correct comparison group
Bibliographic record
Abstract
Previous injury is a well-established predictor of subsequent injury in sports medicine. Some have interpreted this to mean that either our current methods of rehabilitation are inadequate or there is some permanent damage to the tissue and 100% rehabilitation is not possible. In 2011, we illustrated that these analyses and interpretations failed to account for the fact that some athletes are more prone to get injured, either physiologically, or because of their role/type of play. We suggested that the appropriate analysis would simply require using statistical methods that measured how each individual athlete's risk changed from preinjury to postinjury.In this paper, we revisit our recommendation and illustrate that it too would be flawed if the risk of injury changed over time independent of an injury ever occurring. This might be expected if general fitness were to decline over the season, or if the style of play changed between early season games and postseason championship games. Acknowledging that risk may change regardless of whether an injury occurred or not leads to three different general definitions of 100% rehabilitation: (1) a return to the baseline state, (2) a return to the immediate preinjury state and (3) a return to the state that would have been present had the initial injury never occurred. We guide the reader on how to estimate the risks for each definition and the assumptions that must be acknowledged.
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 | 0.035 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".