Do not throw the baby out with the bathwater; screening can identify meaningful risk factors for sports injuries
Bibliographic record
Abstract
Norway’s Professor Roald Bahr recently highlighted that screening does not predict which athlete will sustain an injury.1 Some interpreted this to mean screening is useless for injury prevention. However, screening remains essential in our efforts to protect athletes’ health. To extend what has been a robust discussion, we argue how screening can be important for an individual athlete , and offer potential reasons why and how individual screening tests still lack clinical utility. Previous injury is a well-established injury risk factor. Figure 1 shows data on ACL (re)injuries from Krosshaug et al .2 Applying a traditional (predictive) diagnostic test on these data yields unimpressive results; a positive predictive value of only 29%, with most injuries occurring in previously uninjured athletes. If effective interventions target only previously injured athletes, it would be withheld from the majority of athletes who could benefit. Consequently, we agree with Bahr,1 that all athletes receive effective interventions. Figure 1 The relationship between unilateral ACL injuries and ACL reinjuries in a multiseason prospective cohort study in female football and handball players. (Based on original data from Krosshaug et al .2 But consider another perspective on the same risk factor ‘previous injury’. Regardless of the low predictive value, previous injury is …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".