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 …
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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".