Examining Computerized Software Reliability to Measure Individual Exposure Time
Bibliographic record
Abstract
OBJECTIVE: Epidemiological sport injury research lacks relevance when all athletes are assumed to have equal time exposed to risk. Because athletes do not play equal minutes in ice hockey games, it is important to control for players' individual exposure times (IETs) when studying risk factors for injury. DESIGN: Cohort study. SETTING: Hockey games. PARTICIPANTS: Twenty-eight Minnesota Junior A hockey players. INTERVENTIONS: Individual exposure times were measured on all players dressed for their home games using both a manual (game clock, paper, and pencil) and a computer-based system [Time on Ice (TOI) software]. A sample of matched records was evaluated to compare the 2 methods of recording exposure. MAIN OUTCOME MEASURES: Values of individual player exposure times obtained by TOI software designed for hockey and the manual recording method were compared. RESULTS: Individual exposure times were measured simultaneously by computer-based and manual methods. For 26 games, it would require 156 hours to determine IET per game by the manual method. Conversely, IET totals on TOI software were computed automatically for each player per game. When IET was compared across periods and games, the computer analysis consistently totaled more IET than the manual method. CONCLUSIONS: Time on Ice software was user friendly, required no postgame processing, and showed a high degree of correlation to manually recorded times, although consistently higher IET per player per period than the manual method was noted.
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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 | 0.015 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".