Systematic Review of Clinical and Performance Outcome Measures Reported for Softball Pitchers
Bibliographic record
Abstract
Abstract Fastpitch softball is popular among adolescent and collegiate female athletes. Softball pitchers are susceptible to overuse injuries, and clinical and performance outcome measures can be used to evaluate injury risk and readiness to return to play. Our purpose was to examine clinical and performance-related outcome measures in pitchers using a systematic review of the softball literature published since 1990. PubMed, Embase, CINAHL, and SPORTDiscus databases were searched using the term “softball” AND “pitching” OR “injuries”. Inclusion criteria were studies reporting clinical or performance outcomes like strength, range of motion, anthropometrics, and patient-reported measures. A preliminary screening of studies was completed based on abstracts. Full-text articles were reviewed by two reviewers. Thirty-seven studies met all inclusion criteria. The risk of bias was low for all included studies. Studies reporting body composition (n = 4), range of motion (n = 10), strength (n=12), functional testing (n=4), and patient-reported outcomes (n=3) were included in data extraction. There was a high degree of variability in outcome measures used to evaluate softball pitchers. Ten case studies were included in the discussion of results. Researchers would benefit from a standardized list and protocol for clinical and performance outcome measures used for softball pitchers. This systematic review identifies important gaps in the literature.
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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 | 0.024 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".