Anterior cruciate ligament laxity related to the menstrual cycle: an updated systematic review of the literature.
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to conduct a systematic review regarding the purported differences in anterior cruciate ligament (ACL) laxity throughout the course of the menstrual cycle. METHODS: A systematic review was performed by searching electronic databases, along with hand-searching of journals and reference tracking for any study that assessed ACL integrity throughout the menstrual cycle from 1998 until 2011. Studies that met the pre-defined inclusion criteria were evaluated using the Modified Sackett Score (MSS) instrument that assessed their methodological quality. RESULTS: Thirteen articles out of a possible 28 met the inclusion criteria. CONCLUSIONS: This systematic review found 13 clinical trials investigating the effect of the menstrual cycle on ACL laxity. There is evidence to support the hypothesis that the ACL changes throughout the menstrual cycle, with it becoming more lax during the pre-ovulatory (luteal) phase. Overall, these reviews found statistically significant differences for variation in ACL laxity and injury throughout the menstrual cycle, especially during the pre-ovulatory phase. Female athletes may need to take precautions in order to reduce the likelihood of ACL injury. However, the quality of the assessments was low and the evidence is still very limited. More and better quality research is needed in this area.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".