Bibliographic record
Abstract
OBJECTIVE: The purpose of this article was to evaluate the clinical and basic science evidence surrounding the hypothesis that stretching improves performance. DATA SOURCES AND SELECTION: MEDLINE and Sport Discus were searched using MeSH and textwords for English-language and French-language articles related to stretching and performance (or performance tests). Additional references were reviewed from the bibliographies and from citation searches on key articles. All articles related to stretching and performance (or performance tests) were reviewed. MAIN RESULTS: Of the 23 articles examining the effects of an acute bout of stretching, 22 articles suggested that there was no benefit for the outcomes isometric force, isokinetic torque, or jumping height. There was 1 article that suggested improved running economy. Of 4 articles examining running speed, 1 suggested that stretching was beneficial, 1 suggested that it was detrimental, and 2 had equivocal results. Of the 9 studies examining the effects of regular stretching, 7 suggested that it was beneficial, and the 2 showing no effect examined only the performance test of running economy. There were none that suggested that it was detrimental. CONCLUSIONS: An acute bout of stretching does not improve force or jump height, and the results for running speed are contradictory. Regular stretching improves force, jump height, and speed, although there is no evidence that it improves running economy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".