The Prevention of Sport Injury: An Analysis of 12 000 Published Manuscripts
Bibliographic record
Abstract
OBJECTIVE: To identify the nature and extent of research in sport injury prevention with respect to 3 main categories: (1) training, (2) equipment, and (3) rules and regulations. DATA SOURCES: We searched PubMed, CINAHL, Web of Science, Embase, and SPORTDiscus to retrieve all sports injury prevention publications. Articles were categorized according to the translating research into injury prevention practice model. RESULTS: We retrieved 11 859 articles published since 1938. Fifty-six percent (n = 6641) of publications were nonresearch (review articles and editorials). Publications documenting incidence (n = 1354) and etiology (n = 2558) were the most common original research articles (33% of total). Articles reporting preventive measures (n = 708) and efficacy (n = 460) were less common (10% of the total), and those investigating implementation (n = 162) and effectiveness (n = 32) were rare (1% of total). Six hundred seventy-seven studies focused on equipment and devices to protect against injury, whereas 551 investigated various forms of physical training related to injury prevention. Surprisingly, publications studying changes in rules and regulations aimed at increasing safety and reducing injuries were rare (<1%; n = 63) with a peak of only 20 articles over the most recent 5-year period and an average of 10 articles over the preceding 5-year blocks of time. CONCLUSIONS: Only 492 of 11 859 publications actually assessed the effectiveness of sports injury prevention interventions or their implementation. Research in the area of regulatory change is underrepresented and might represent one of the greatest opportunities to prevent injury.
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 | 0.036 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.109 | 0.087 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".