Systematic review of the statistical scope used in studies based on skeletal muscle autophagy and exercise
Bibliographic record
Abstract
Skeletal muscle reaction to exercise is an essential are of research due to its ongoing prevalence in disease research and general health. It is well documented that under exercise conditions, biogenesis and autophagy increase. One main component of this pathway are lysosomes, the essential cellular clearance machine. Statistical analyses used to analyze these data have been sustained over the years. The objective of this systematic review is to compare and contrast the different methods used for analyzing data in molecular exercise physiology. Upon investigating the research papers the majority of the papers used either a t-test or an ANOVA as their primary statistical analyses, used 41% and 64% of the time, respectively. All other statistical tests were used a maximum of 9% of the time. Another trend that was evident was the increased utilization of post hoc tests in the more recent papers compared to earlier papers. This could provide interesting evidence into the credibility of the results reported and provide more insight into the research in molecular exercise physiology.
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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 | 0.094 | 0.411 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.030 | 0.032 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".