Movement Demands in Australian Rules Football as Indicators of Muscle Damage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study was to determine if there is an association between variables that describe movements in an Australian Rules football (ARF) game with muscle damage. Fourteen elite junior ARF players were monitored with a global positioning system (GPS) during a match, and muscle damage was estimated by determining creatine kinase (CK) 24 hours postmatch. The players were median split based on CK levels, into a high and low CK group, and the groups were compared with independent t-tests. The primary finding was that the group that experienced greater muscle damage (high CK group) generally covered significantly (p < 0.05) greater distances. This was the case for running speeds between 4 and 7 m·s(-1) and, with the exception of high acceleration, all intensities of acceleration and deceleration. The high, as compared with the low, CK group also produced a significantly greater (42%) "player load." All of these significant differences were accompanied by large effect sizes. Group-specific Pearson (r) correlations between CK level and GPS variables suggest that a certain volume of movement is required before the elicitation of a positive relationship beyond trivial or small. Correlations between CK and running speeds >4 m·s(-1) and moderate-high acceleration and deceleration were negative in the low CK (lesser volumes) group. With the exception of low-intensity acceleration/deceleration, the same relationships were positive and generally of a moderate-to-large magnitude in the high CK (greater volumes) group. It may be that a certain volume of movement is required for that movement to be strongly associated with CK levels. It was concluded that selected GPS variables obtained from ARF games can be used as indicators of muscle damage, and this information may be used to individualize recovery strategies after games.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it