The Limitations of Systematic Reviews With Meta-Analyses in Sport Science
Bibliographic record
Abstract
It is generally argued that systematic reviews are necessary because of the possibility of some subjective bias in narrative reviews. Thus, SRwMs are considered the gold standard of scientific evidence. However, “all that glitters is not gold,”2 as SRwMs are only as good as the articles they contain, not only from a risk-of-bias perspective2 but also when considering the characteristics of the samples, protocols, and outcomes included. Generalizations may erroneously pretend to be a one-size-fits-all solution for complex biological phenomena. When considering the factors and potential moderators of training interventions and subsequent performance and physiological adaptations, a suite of population characteristics (eg, training background, age, sex), training-regimen characteristics (eg, exercise type and mode, loading, timing), testing protocols and selected outcomes (eg, exercise type, performance parameter, timing), and other contextual factors should be included. Consideration of all these factors is important for a better characterization of any (acute or chronic) training effect on an individual athlete basis, as previously exemplified for postactivation performance-enhancement strategies.3 However, this approach is not always possible for most SRwMs and the included studies. This shortcoming means that any analysis from an SRwM may be limited because of the infinite combinations of all those potential factors and their moderators, which may result in suboptimal combinations.
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.
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 | 0.262 | 0.632 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".