Bibliographic record
Abstract
Background A cornerstone of surveillance is the timely dissemination of information to those needing to know. However, injury data is often noisy and the qualitative assessment of trends can be problematic making the dissemination of information difficult. A quantitative method is needed to objectively characterise injury trends. Aims/Objectives/Purpose This study evaluated the applicability of JoinPoint software developed for cancer research to injury data collected by the Canadian Hospitals Injury Reporting and Prevention Programme (CHIRPP). Specifically, JoinPoint regression was used to quantify trends and identify important inflection points (ie, elbows) in a test-dataset. Methods The test-dataset consisted of all hockey-related brain injuries reported to CHIRPP between 1990 and 2009. Hockey injuries were chosen because the general temporal pattern is well known, which would facilitate evaluation of the method. JoinPoint statistical software was used to quantify injury trends across calendar months. Statistical significance of inflection points was tested using Monte Carlo permutation methods. Results/Outcomes Between 1990 and 2009, there were 5868 hockey related brain injuries reported to CHIRPP. Given that hockey is a winter sport, 71% of the cases were reported between the winter months. The elbows (inflection points) were found in August where the number of cases began to rise until peaked in November. The average annual percent change of 51% was significantly different from zero at α=0.05. Significance/Contribution to the Field From the methodological perspective, we demonstrated the utility of JoinPoint regression to CHIRPP data on hockey-related brain injuries. Surveillance information extracted using JoinPoint could be used in evidence-based policy and prevention efforts.
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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 | 0.018 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".