Bibliographic record
Abstract
Lies, damned lies and statistics To the Editor; I commend Forward et al (2) for their interesting and thought-provoking article based on data from emergency department visits due to hockey injury reported according to sex and injury type. Many additional factors, not mentioned in their discussion, should be considered in interpreting their data. First, the differences between the way parents view injury for girls and boys may influence whether they choose to go to the emergency department, wait to see a family doctor or do not turn to a medical professional (3). Second, males are less likely to seek help for medical care in general (4), making emergency department visits for soft tissue injury more likely for females. Third, where and if help is sought is dependent on social factors (5), and whether there is a trainer or medical professional present at games or practices who might treat injury on site without requiring further medical attention (6). Women’s teams are less well funded, less supported by the media, less socially accepted and less likely to have a trainer or medical professional present at games and practices (3,6–8). Men are more likely to have an athletic trainer at practices and games, with an understanding of injury prevention exercise, warm-ups and the care of soft tissue injuries, perhaps circumventing the need to present to an emergency department (6). Reporting that injury rates are higher in females than males without considering the societal factors that affect the reporting of these injuries supports the misogynist view that girls are too delicate for sport, especially a fast-paced and ‘dangerous’ sport such as hockey (9). An acknowledgement of these considerations would enhance the discussion presented in this article and lead to a deeper understanding of this complicated issue.
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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 | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.038 | 0.021 |
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".