Shouldering the load: A scoping review of incidence, types, and risk factors of shoulder injuries in weight-lifting athletes
Bibliographic record
Abstract
Lifestyles advocating for proper health and fitness have been trending in recent years, and as such, sports like weightlifting have become very popular worldwide. While these sports improve physical fitness and cardiovascular health, they carry an inherent risk for physical injuries, mainly to the shoulder. In this review, we aimed to explore the epidemiology of shoulder injuries in weightlifting using a systematic search of the literature. The databases PubMed, Google Scholar (pages 1-20), Embase, and SPORTDiscus were queried using relevant search terms to extrapolate all studies pertaining to shoulder injuries in these two sports. Shoulder injuries turned out to be common in both sports with varying incidence rates and were shown to occur to athletes independent of gender and age. Anterior instability and overuse injuries were the most common injury types, and presentation varied with regards to symptoms and severity. Both intrinsic and extrinsic factors can contribute to shoulder injuries in the setting of these two sports, including incorrect implementation of techniques, age, vulnerable positioning of the shoulder during the lift, and overtraining which leads to overuse injuries.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".