Bibliographic record
Abstract
Objectives To quantify acute injuries sustained during weightlifting that result in training restrictions and identify potential risk factors or preventative factors in Master athletes and to evaluate potentially complex interactions of age, sex, health-related and training-related predictors of injuries with machine learning (ML) algorithms. Methods A total of 976 Masters weightlifters from Australia, Canada, Europe and the USA, ages 35–88 (51.1% women), completed an online survey that included questions on weightlifting injuries, chronic diseases, sport history and training practices. Ensembles of ML algorithms were used to identify factors associated with acute weightlifting injuries and performance of the prediction models was evaluated. In addition, a subgroup of variables selected by six experts were entered into a logistic regression model to estimate the likelihood of an injury. Results The accuracy of ML models predicting injuries ranged from 0.727 to 0.876 for back, hips, knees and wrists, but were less accurate (0.644) for shoulder injuries. Male Master athletes had a higher prevalence of weightlifting injuries than female Master athletes, ranging from 12% to 42%. Chronic inflammation or osteoarthritis were common among both men and women. This was associated with an increase in acute injuries. Conclusions Training-specific variables, such as choices of training programmes or nutrition programmes, may aid in preventing acute injuries. ML models can identify potential risk factors or preventative measures for sport injuries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.003 |
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".