084 Monitoring workload to evaluate injury risk: the impact of missing data
Bibliographic record
Abstract
Background The acute: chronic workload ratio (ACWR) is used to monitor workload, with both low and high ACWR associated with injury risk. Ignoring or imputing missing data points may influence ACWR calculations. Objective To examine the effect of ignoring versus imputing missing data on ACWR. Design Cohort, longitudinal. Setting Youth basketball. Participants Fifty (25F, 25M; 16.5 years; 66.2 kg; 173.5 cm) basketball players on four high school teams. Assessment of Risk Factors Participants wore a jump counter (VERT® Classic) to record external workload during practices and games throughout the 17-week season. Main Outcome Measurements Two datasets were created: missing data were ignored, and missing data were imputed using a machine learning algorithm based on typical jump counts for the individual, team and sex. The distribution of ACWR was compared between datasets using a two-sample Kolmogorov-Smirnov test. Pearson correlations were used to assess how the ACWR for the ignored and imputed datasets relate to the difference between the percent of missing acute and chronic data. Results The distribution of ACWR was significantly different between the ignored and imputed datasets (D=0.164, p<0.001). The ignored dataset had 40% more cases of ACWR<0.5 and 97% more cases of ACWR>2.0 than the imputed dataset. There was a significant moderate association between ACWR and the difference between the percent of missing acute and chronic data for the ignored dataset (rho=0.617, p<0.001). When more acute than chronic data are missing, ACWR is low; when more chronic than acute data are missing, ACWR is high. There was no relationship between missing data and ACWR for imputed data (rho=0.061, p=0.147). Conclusions When missing data are ignored, ACWR is dependent on the quantity of missing acute and chronic data. Additionally, ignoring rather than imputing missing data is likely to result in more extreme ACWR, which could influence evaluation of the relationship between workload and injury risk.
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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 | 0.037 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".